Protein biomarker panels for detecting colorectal cancer and advanced adenoma

ABSTRACT

Disclosed herein are panels related to the diagnosis or recognition of colon and colorectal cancer in a subject. The disclosed panels and related methods are used to predict or assess colon tumor status in a patient. They can be used to determine nature of tumor, recurrence, or patient response to treatments. Some embodiments of the methods include generating a report for clinical management.

RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.15/629,593, filed Jun. 21, 2017, which is a continuation of U.S.application Ser. No. 15/094,767, filed Apr. 8, 2016, now U.S. Pat. No.9,689,874, which claims the benefit of U.S. Provisional Application Ser.No. 62/146,158, filed Apr. 10, 2015, U.S. Provisional Application Ser.No. 62/160,560, filed May 12, 2015, U.S. Provisional Application Ser.No. 62/165,846, filed May 22, 2015, U.S. Provisional Application Ser.No. 62/196,889, filed Jul. 24, 2015, and U.S. Provisional ApplicationSer. No. 62/239,771, filed Oct. 9, 2015, which are all herebyincorporated by reference in their entirety.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has beensubmitted electronically in ASCII format and is hereby incorporated byreference in its entirety. Said ASCII copy, created May 1, 2019, isnamed 53897-738-302-SL.txt and is 106 KB in size.

BACKGROUND OF THE INVENTION

Colorectal cancer (CRC) can result from uncontrolled cell growth in thecolon or rectum (parts of the large intestine), or in the appendix. CRCcan develop from a colon polyp. A colon polyp typically comprises abenign clump of cells that forms on the lining of the large intestine orrectum. While many colon polyps are non-malignant, a polyp can developinto an adenoma. Colorectal adenomas can then grow into advancedcolorectal adenomas, which can then develop into CRC.

Colorectal cancer is a leading cause of cancer-related deaths in theUnited States with over 142, 820 diagnosed cases and over 50,000 deathsin 2013. According to a 2011 study, there are an estimated 1.2 milliondiagnoses per year and 600,000 deaths worldwide. CRC is one of the mostpreventable cancers given its typically slow progression from earlystages to metastatic disease and available tools for its diagnosis, butit is one of the least prevented cancers. This is at least partly due tothe poor compliance with available screening by patients due to theinvasive or unpleasant nature of the current screening tests.

The risk of developing CRC increases with age. Ninety percent of newcases and 93% of deaths occur in people age 50 and older. During their60s, men have a 10-fold increased risk of developing CRC compared totheir 40s. Regular screening allows for the removal of advancedcolorectal adenomas or precancerous polyps and detection of early stagecancer, which is the key factor in the effective treatment of thedisease.

The survival rate for patients diagnosed with CRC is highly dependent onwhen it is caught. CRC usually progresses through four stages, definedas Stage I through Stage IV. Stages I and II are local stages, duringwhich aberrant cell growth is confined to the colon or rectum. Stage IIIis a regional stage, meaning the cancer has spread to the surroundingtissue but remains local. Stage IV is distal and indicates that thecancer has spread throughout the other organs of the body, most commonlythe liver or lungs. It is estimated that the five-year survival rate isover 90% for those patients who were diagnosed with Stage I CRC,compared to 13% for a Stage IV diagnosis. If caught early, CRC istypically treated by surgical removal of the cancer. After the cancerspreads, surgical removal of the cancer is typically followed bychemotherapy

Colonoscopy and sigmoidoscopy remain the gold standard for detectingcolon cancer. However, the highly invasive nature and the expense ofthese exams contribute to low acceptance from the population.Furthermore, such highly invasive procedures expose subjects to risk ofcomplications such as infection.

The most common non-invasive test for colorectal cancer is the fecaloccult blood test (“FOBT”). Unfortunately, in addition to its highfalse-positive rate, the sensitivity of the FOBT remains around 50% andmay have less sensitivity for detection of early stage CRC. Numerousserum markers, such as carcinoembryonic antigen (“CEA”), carbohydrateantigen 19-9, and lipid-associated sialic acid, have been investigatedin colorectal cancer. However, their low sensitivity has induced theAmerican Society of Clinical Oncology to state that none can berecommended for screening and diagnosis, and that their use should belimited to post-surgery surveillance.

Because of the significantly increased chance of survival if CRC isdetected early in the disease progression, CRC is one of three cancersfor which the American Cancer Society, or ACS, recommends routinescreening (breast and cervical cancer are the others). In the UnitedStates, screening for CRC is currently recommended by the ACS and theU.S. Preventative Services Task Force, or USPSTF, for all men and womenaged 50-75 using fecal occult blood testing, or FOBT, which is a fecaltest, or one of two procedures: colonoscopy or sigmoidoscopy. Despitethe benefits of routine screening on improving five-year survival ratesif CRC is diagnosed early, the rate of screening compliance is low duein part to the limitations of existing solutions.

SUMMARY OF THE INVENTION

Provided herein are methods of assessing a colorectal cancer status inan individual. Also provided herein are methods of assessing acolorectal cancer risk status in a blood sample of an individual. Somesuch methods comprise the steps of obtaining a circulating blood samplefrom the individual; obtaining a biomarker panel level for a biomarkerpanel comprising a list of proteins in the sample comprising AACT, CO3,CO9, MIF, and PSGL to comprise panel information from said individual;comparing said panel information from said individual to a referencepanel information set corresponding to a known colorectal cancer status;and categorizing said individual as having said colorectal cancer statusif said individual's reference panel information does not differsignificantly from said reference panel information set. Various aspectsof these methods are recited below, contemplated as distinct or incombination. Methods are also contemplated to include methods whereinobtaining a circulating blood sample comprises drawing blood from a veinor artery of the individual. Methods are also contemplated to includemethods wherein the panel information comprises age information for theindividual. Optionally, the list of proteins comprises AACT, CO3, CO9,MIF, PSGL, CATD, CEA and SEPR. Optionally, the list of proteinscomprises no more than 15 proteins. In some cases the list comprisesmore than 8 proteins, where in a CRC signal is derived from the list ofproteins comprising AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR.Optionally, the list of proteins comprises no more than 8 proteins. Insome cases, the list of proteins comprises AACT, CO3, CO9, MIF, PSGL,CATD, CEA and SEPR. Optionally, the categorizing has a sensitivity of atleast 81% and a specificity of at least 78%. Methods are alsocontemplated to comprise transmitting a report of results of saidcategorizing a health practitioner. Optionally, the report indicates asensitivity of at least 81%. Optionally, the report indicates aspecificity of at least 78%. Optionally, the report recommends that acolonoscopy be performed. Optionally, the individual undergoes acolonoscopy. Optionally, the report recommends an independent surgicalintervention. Optionally, the individual undergoes an independentsurgical intervention. Optionally, the report recommends undergoing anindependent cancer assay. Optionally, the individual undergoes anindependent cancer assay. Optionally, the report recommends undergoing astool cancer assay. Optionally, the individual undergoes a stool cancerassay. Optionally, the report recommends administering an anticancercomposition. Optionally, an anticancer composition is administered tothe individual. Optionally, the report recommends continued monitoring.Optionally, at least one biomarker level of said individual's panelinformation differs significantly from a corresponding value from saidreference panel, and wherein said individual's panel level as a wholedoes not differ significantly from said reference panel level. Alsocontemplated herein are methods wherein no parameter of saidindividual's reference panel information in isolation is indicative ofsaid colorectal cancer status in said individual at a sensitivity ofgreater than 65% or a specificity of greater than 65%. Optionally, theobtaining protein levels comprises contacting a fraction of thecirculating blood sample to a set of antibodies, wherein the set ofantibodies comprises antibodies specific to AACT, CO3, CO9, MIF, andPSGL. Optionally, the obtaining protein levels comprises subjecting afraction of the circulating blood sample to a mass spectrometricanalysis. Optionally, at least one of said comparing and saidcategorizing is performed on a computer configured to analyze referencepanel information. Optionally, said reference panel information setcorresponding to a known colorectal cancer status comprises a product ofa machine learning model. Optionally, the machine learning model istrained using at least 100 biomarker panels corresponding to knowncolorectal health status. Panels disclosed herein distinguish sampleshaving a CRC signal not only from samples from healthy individuals butalso from samples from individuals having other types cancer or othercell cycle or cell proliferation aliments, as indicated in FIG. 4.

Also provided herein are methods of monitoring efficacy of a colorectalcancer treatment regimen in an individual. Some such methods comprisethe steps of obtaining a first sample comprising circulating blood fromthe individual at a first time point; administering the treatmentregimen to the individual; obtaining a second sample comprisingcirculating blood from the individual at a second time point; obtaininga first panel level comprising protein levels for a list of proteins inthe first sample and a second panel level comprising protein levels fora list of proteins in the second sample, said list comprising AACT, CO3,CO9, MIF, and PSGL to comprise panel information for said first sampleand said second sample; wherein a change in protein levels indicatesefficacy of the colorectal cancer treatment. Also provided herein are exvivo methods of monitoring efficacy of a colorectal cancer treatment inan individual. Some such methods comprise the steps of obtaining a firstsample comprising circulating blood from the individual at a first timepoint; obtaining a second sample comprising circulating blood from thesame individual receiving a colorectal cancer treatment at a second timepoint; obtaining a first panel level comprising protein levels for alist of proteins in the first sample and a second panel level comprisingprotein levels for a list of proteins in the second sample, said listcomprising AACT, CO3, CO9, MIF, and PSGL to comprise panel informationfor said first sample and said second sample; wherein a change inprotein levels indicates efficacy of the colorectal cancer treatment.Various aspects of these methods are recited below, contemplated asdistinct or in combination. Methods are contemplated to includeobtaining the first sample comprises drawing blood from a vein or arteryof the individual. Optionally, the colorectal cancer treatment ortreatment regimen comprises administration of a pharmaceuticalcomposition. Optionally, the colorectal cancer treatment or treatmentregimen comprises administration of a chemotherapeutic agent.Optionally, the colorectal cancer treatment or treatment regimencomprises a colonoscopy. Optionally, the colorectal cancer treatment ortreatment regimen comprises a polypectomy. Optionally, the colorectalcancer treatment or treatment regimen comprises radiotherapy. Methodsare also contemplated to include methods comprising comparing said firstsample panel level and said second panel level to at least one panellevel of a healthy reference, wherein the second sample panel levelbeing more similar to the panel level of the healthy reference indicatesefficacy of the colorectal cancer treatment. Optionally, methodscomprise said first sample panel level and said second panel level to atleast one panel level of a healthy reference, wherein the first samplepanel level being more similar to the panel level of the colorectalcancer reference indicates efficacy of the colorectal cancer treatment.Optionally, the list of proteins comprises AACT, CO3, CO9, MIF, PSGL,CATD, CEA and SEPR. Optionally, the list of proteins comprises no morethan 15 proteins. Optionally, the list of proteins comprises no morethan 8 proteins. Optionally, the list of proteins comprises AACT, CO3,CO9, MIF, PSGL, CATD, CEA and SEPR. Optionally, methods comprisechanging the colorectal cancer treatment or treatment regimen if noefficacy is indicated. Optionally, methods comprise repeating colorectalcancer treatment or the treatment regimen if no efficacy is indicated.Optionally, methods comprise continuing the colorectal cancer treatmentor treatment regimen if no efficacy is indicated. Optionally, methodscomprise discontinuing the colorectal cancer treatment or treatmentregimen if efficacy is indicated.

Also provided herein are panels of proteins indicative of anindividual's colorectal cancer status. Some such panels comprise atleast 5 proteins selected from the list consisting of AACT, CO3, CO9,MIF, PSGL, CATD, CEA and SEPR, wherein measurement of the panel at alevel that does not differ significantly from a reference panel fromcirculating blood of an individual is indicative of the individual'scolorectal cancer status corresponding to a reference panel colorectalcancer status at a sensitivity of at least 81% and a specificity of atleast 78%; and wherein no constituent protein level of said panel isindicative of the individual's colorectal cancer status at a sensitivityof greater than 65% and a specificity of greater than 65%. Variousaspects of these panels are recited below, contemplated as distinct andin combination. Panels are contemplated to comprise at least 6 proteinsselected from the list consisting of AACT, CO3, CO9, MIF, PSGL, CATD,CEA and SEPR. Optionally, panels comprise no more than 12 proteins, ofwhich at least 4 proteins selected from the list consisting of AACT,CO3, CO9, MIF, PSGL, CATD, CEA and SEPR. Optionally, panels comprise nomore than 12 proteins, wherein the panel of proteins comprises AACT,CO3, CO9, MIF, PSGL, CATD, CEA and SEPR. Optionally, panels consist ofAACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR. Also contemplated hereinare any of the abovementioned panels of proteins for use in assessing acolorectal cancer status according to any of the above methods ormonitoring efficacy of a colorectal cancer treatment according to any ofthe above methods.

Also provided herein are kits comprising an antibody panel, saidantibody panel comprising antibodies that identify at least 5 proteinsselected from the list consisting of AACT, CO3, CO9, MIF, PSGL, CATD,CEA and SEPR. Various aspects of these kits are recited below,contemplated as distinct or in combination. Kits are contemplated tocomprise an antibody that binds to a control protein. Optionally, kitscomprise no more than 15 antibodies. Optionally, kits comprise no morethan 12 antibodies. Optionally, said antibody panel comprises antibodiesthat identify all of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR.Optionally kits comprise instructions functionally related to use of thekit to assess a patient colorectal cancer status. Also contemplatedherein are any of the abovementioned kits for use in assessing acolorectal cancer status according to any of the above methods ormonitoring efficacy of a colorectal cancer treatment according to any ofthe above methods.

Also contemplated herein are computer systems configured to assess acolorectal cancer risk in an individual. Some such computer systemscomprise a memory unit for receiving data comprising measurement of apanel of proteins comprising at least 5 proteins selected from the listconsisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR from abiological sample comprising circulating blood, computer-executableinstructions for assessing a colorectal cancer risk associated with saidmeasurement of said panel of proteins, an output unit for delivering areport assessing said colorectal cancer risk associated with saidmeasurement of said panel of proteins. Optionally, said panel comprisesat least 6 proteins selected from the list consisting of AACT, CO3, CO9,MIF, PSGL, CATD, CEA and SEPR. Optionally, said panel comprises no morethan 12 proteins, of which at least 5 proteins selected from the listconsisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR. Optionally,said panel comprises no more than 12 proteins, wherein the panel ofproteins comprises AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR.Optionally, said panel consists of AACT, CO3, CO9, MIF, PSGL, CATD, CEAand SEPR. Optionally, the memory unit is configured for receiving datacomprising measurement of a second panel of proteins. Optionally, saiddata comprising measurement of a panel of proteins comprises ELISA data.Optionally, said data comprising measurement of a panel of proteinscomprises mass spectrometry data. Optionally, assessing a colorectalcancer risk comprises comparing said data to a reference panelassociated with a known colorectal cancer status. Optionally, saidindividual is assigned said known colorectal cancer status when saiddata does not differ significantly from said reference panel.Optionally, said reference panel indicates presence of colorectalcancer. Optionally, said reference panel indicates absence of colorectalcancer. Optionally, assessing a colorectal cancer risk is performed on acomputer configured to analyze reference panel information. Optionally,said memory unit comprises at least one reference panel information setcorresponding to a known colorectal cancer status. Optionally, the atleast one reference panel information set comprises a machine learningmodel. Computer systems are also contemplated wherein the machinelearning model is trained using at least 100 biomarker panelscorresponding to known colorectal health status. Optionally, said reportindicates a sensitivity of at least 81% and a specificity of at least78%. Optionally, said report indicates a sensitivity of at least 81%.Optionally, said report indicates a specificity of at least 78%.Optionally, said report recommends that a colonoscopy be performed.Optionally, said report recommends an independent surgical intervention.Optionally, said report recommends undergoing an independent cancerassay. Optionally, said report recommends undergoing a stool cancerassay. Optionally, said report recommends administering an anticancercomposition. Optionally, said report recommends continued monitoring.Computer systems herein are also contemplated wherein at least oneparameter of said individual's reference panel information differssignificantly from a corresponding value from said reference panelinformation set, and wherein said individual's reference panelinformation does not differ significantly from said reference panelinformation set. Optionally, no single protein of said panel indicatesthe individual's colorectal cancer status at a specificity of greaterthan 65% or a sensitivity of greater than 65%. Optionally, the memoryunit is configured to receive age information from said individual.Optionally, the computer-executable instructions factor in age of theindividual when assessing said colorectal cancer risk associated withsaid measurement of said panel of proteins.

Also provided herein are methods of assessing an advanced adenoma riskstatus in an individual. Also provided herein are methods of assessingan advanced adenoma risk status in a blood sample of an individual. Somesuch methods include comprising the steps of obtaining a circulatingblood sample from the individual; obtaining protein levels for a list ofproteins relevant to advanced adenoma in the sample comprising at leastthree of CATD, CLUS, GDF15 and SAA1 to comprise biomarker panelinformation from said individual; comparing said panel information fromsaid individual to a reference panel information set corresponding to aknown advanced adenoma status; and categorizing said individual ashaving said advanced adenoma risk status if said individual's referencepanel information does not differ significantly from said referencepanel information set. Various aspects of these methods are recitedbelow, contemplated as distinct or in combination. Methods herein arecontemplated to include obtaining a circulating blood sample comprisesdrawing blood from a vein or artery of the individual. Optionally, thepanel information comprises age information for the individual.Optionally, the list of proteins comprises no more than 15 proteins.Optionally, the list of proteins comprises no more than 5 proteins.Optionally, list of proteins comprises CATD, CLUS, GDF15 and SAA1.Optionally, the categorizing has a sensitivity of at least 50% and aspecificity of at least 80%. Optionally, the categorizing has asensitivity of at least 47% and a specificity of at least 83%.Optionally, the categorizing has a sensitivity of at least 47% and aspecificity of at least 80%. Optionally, methods herein comprisetransmitting a report of results of said categorizing to a healthcareprofessional. Optionally, the report indicates a sensitivity of at least47%. Optionally, the report indicates a sensitivity of at least 50%.Optionally, the report indicates a specificity of at least 80%.Optionally, the report recommends that a colonoscopy be performed.Optionally, the individual undergoes a colonoscopy. Optionally, thereport recommends an independent surgical intervention. Optionally, theindividual undergoes an independent surgical intervention. Optionally,the report recommends undergoing an independent cancer assay.Optionally, the individual undergoes an independent cancer assay.Optionally, the report recommends undergoing a stool cancer assay.Optionally, the individual undergoes a stool cancer assay. Optionally,the report recommends administering an anticancer composition.Optionally, an anticancer composition is administered to the individual.Optionally, the report recommends continued monitoring. Methods are alsocontemplated herein wherein at least one parameter of said individual'sreference panel differs significantly from a corresponding value fromsaid reference panel set, and wherein said individual's reference panelinformation as a whole does not differ significantly from said referencepanel information set. Optionally, methods are contemplated wherein noparameter of said individual's reference panel information in isolationis indicative of said advanced adenoma status in said individual at asensitivity of greater than 65% or a specificity of greater than 65%.Optionally, the obtaining protein levels comprises contacting a fractionof the circulating blood sample to a set of antibodies, wherein the setof antibodies comprises antibodies specific to CATD, CLUS, GDF15 andSAA1. Optionally, the obtaining protein levels comprises subjecting afraction of the circulating blood sample to a mass spectrometricanalysis. Optionally, the obtaining protein levels comprises contactingthe sample to protein binding DNA aptamers. Optionally, the obtainingprotein levels comprises contacting the sample to an antibody array.Optionally, at least one of said comparing and said categorizing isperformed on a computer configured to analyze reference panelinformation. Optionally, said reference panel information setcorresponding to a known advanced adenoma status comprises is a productof a machine learning model. Optionally, the machine learning model istrained using at least 100 biomarker panels corresponding to knowncolorectal health status.

Also provided herein are methods of monitoring efficacy of an advancedadenoma treatment regimen in an individual. Some such methods comprisethe steps of obtaining a first sample comprising circulating blood fromthe individual at a first time point; administering the treatmentregimen to the individual; obtaining a second sample comprisingcirculating blood from the individual at a second time point; obtaininga first panel level protein levels for a list of proteins relevant toadvanced adenoma assessment in the first sample and a second panel levelprotein levels for a list of proteins relevant to advanced adenomaassessment in the second sample, said list comprising CATD, CLUS, GDF15and SAA1 to comprise panel information for said first sample and saidsecond sample; wherein a change in protein levels indicates efficacy ofthe advanced adenoma treatment. Also provided herein are ex vivo methodsof monitoring efficacy of an advanced adenoma treatment in anindividual. Some such methods comprise the steps of obtaining a firstsample comprising circulating blood from the individual at a first timepoint; obtaining a second sample comprising circulating blood from thesame individual receiving an advanced adenoma treatment at a second timepoint; obtaining a first panel level comprising protein levels for alist of proteins in the first sample and a second panel level comprisingprotein levels for a list of proteins in the second sample, said listcomprising CATD, CLUS, GDF15 and SAA1 to comprise panel information forsaid first sample and said second sample; wherein a change in proteinlevels indicates efficacy of the colorectal cancer treatment. Variousaspects of these methods are recited below, contemplated as distinct orin combination. Methods are also included wherein obtaining the firstsample comprises drawing blood from a vein or artery of the individual.Optionally, the advanced adenoma treatment or treatment regimencomprises administration of a pharmaceutical composition. Optionally,the advanced adenoma treatment or treatment regimen comprisesadministration of a chemotherapeutic agent. Optionally, the advancedadenoma treatment or treatment regimen comprises a colonoscopy.Optionally, the advanced adenoma treatment or treatment regimencomprises a polypectomy. Optionally, the advanced adenoma treatment ortreatment regimen comprises radiotherapy. Methods are also contemplatedcomprising comparing said first sample protein levels and said secondpanel protein levels to protein levels of a healthy reference, whereinthe second sample levels being more similar to the protein levels of thehealthy reference indicates efficacy of the advanced adenoma treatment.Optionally, comparing said first sample protein levels and said secondpanel protein levels to protein levels of an advanced adenoma reference,wherein the first sample levels being more similar to the protein levelsof the advanced adenoma reference indicates efficacy of the advancedadenoma treatment. Optionally, the list of proteins relevant to advancedadenoma assessment comprises CATD, CLUS, GDF15 and SAA1. Optionally, thelist of proteins relevant to advanced adenoma assessment comprises nomore than 12 proteins. Optionally, the list of proteins relevant toadvanced adenoma assessment comprises no more than 8 proteins.Optionally, the list of proteins relevant to advanced adenoma assessmentconsists of CATD, CLUS, GDF15 and SAA1. Optionally, methods hereincomprise changing the advanced adenoma treatment or treatment regimen ifno efficacy is indicated. Also contemplated herein are methodscomprising repeating the advanced adenoma treatment or treatment regimenif no efficacy is indicated. Optionally, methods are contemplated tocomprise continuing the advanced adenoma treatment or treatment regimenif no efficacy is indicated. Optionally, methods are contemplated tocomprise discontinuing the advanced adenoma treatment or treatmentregimen if efficacy is indicated.

Also provided herein are panels of proteins indicative of anindividual's advanced adenoma status. Some such panels are contemplatedto comprise at least 3 proteins relevant to advanced adenoma assessmentselected from the list consisting of CATD, CLUS, GDF15 and SAA1, whereinmeasurement of the panel at a level that does not differ significantlyfrom a reference panel from circulating blood of an individual isindicative of the individual's advanced adenoma status corresponding toa reference panel advanced adenoma status at a sensitivity of at least50% and a specificity of at least 80%; and wherein no constituentprotein level of said panel is indicative of the individual's advancedadenoma status at a sensitivity of greater than 65% and a specificity ofgreater than 65%. Panels are contemplated to comprise proteins relevantto advanced adenoma assessment CATD, CLUS, GDF15 and SAA1.

Also provided herein are kits comprising an antibody panel, saidantibody panel comprising antibodies that identify at least 3 proteinsadvanced adenoma assessment selected from the list consisting of CATD,CLUS, GDF15 and SAA1. Various aspects of these kits are recited below,contemplated as distinct or in combination. Kits are contemplated tocomprise an antibody that binds to a control protein. Optionally, kitscomprise no more than 15 antibodies. Optionally, kits comprise no morethan 12 antibodies. Optionally, said antibody panel comprises antibodiesthat identify all of CATD, CLUS, GDF15 and SAA1. Optionally kitscomprise instructions functionally related to use of the kit to assess apatient advanced adenoma status. Also contemplated herein are any of theabovementioned panels of proteins for use in assessing a colorectalcancer status according to any of the above methods or monitoringefficacy of a colorectal cancer treatment according to any of the abovemethods. Also contemplated herein are any of the abovementioned kits foruse in assessing a colorectal cancer status according to any of theabove methods or monitoring efficacy of a colorectal cancer treatmentaccording to any of the above methods.

Also contemplated herein are computer systems configured to assessadvanced adenoma risk in an individual. Some such computer systemscomprise a memory unit for receiving data comprising measurement of apanel of proteins comprising at least 3 proteins selected from the listconsisting of CATD, CLUS, GDF15 and SAA1 from a biological samplecomprising circulating blood, computer-executable instructions forassessing advanced adenoma risk associated with said measurement of saidpanel of proteins, an output unit for delivering a report assessing saidadvanced adenoma risk associated with said measurement of said panel ofproteins. Optionally, said panel comprises CATD, CLUS, GDF15 and SAA1.Optionally, said panel comprises no more than 12 proteins, of which atleast 5 proteins selected from the list consisting of AACT, CO3, CO9,MIF, PSGL, CATD, CEA and SEPR. Optionally, said panel comprises no morethan 12 proteins, wherein the panel of proteins comprises CATD, CLUS,GDF15 and SAA1. Optionally, said panel consists of CATD, CLUS, GDF15 andSAA1. Optionally, the memory unit is configured for receiving datacomprising measurement of a second panel of proteins. Optionally, saiddata comprising measurement of a panel of proteins comprises ELISA data.Optionally, said data comprising measurement of a panel of proteinscomprises mass spectrometry data. Optionally, assessing a advancedadenoma risk comprises comparing said data to a reference panelassociated with a known advanced adenoma status. Optionally, saidindividual is assigned said known advanced adenoma status when said datadoes not differ significantly from said reference panel. Optionally,said reference panel indicates presence of advanced adenoma. Optionally,said reference panel indicates absence of advanced adenoma. Optionally,assessing a advanced adenoma risk is performed on a computer configuredto analyze reference panel information. Optionally, said memory unitcomprises at least one reference panel information set corresponding toa known advanced adenoma status. Optionally, the at least one referencepanel information set comprises a machine learning model. Computersystems are also contemplated wherein the machine learning model istrained using at least 100 biomarker panels corresponding to knowncolorectal health status. Optionally, said report indicates asensitivity of at least 50% and a specificity of at least 80%.Optionally, said report indicates a sensitivity of at least 50%.Optionally, said report indicates a specificity of at least 80%.Optionally, said report recommends that a colonoscopy be performed.Optionally, said report recommends an independent surgical intervention.Optionally, said report recommends undergoing an independent cancerassay. Optionally, said report recommends undergoing a stool cancerassay. Optionally, said report recommends administering an anticancercomposition. Optionally, said report recommends continued monitoring.Computer systems herein are also contemplated wherein at least oneparameter of said individual's reference panel information differssignificantly from a corresponding value from said reference panelinformation set, and wherein said individual's reference panelinformation does not differ significantly from said reference panelinformation set. Optionally, no single protein of said panel indicatesthe individual's advanced adenoma status at a specificity of greaterthan 65% or a sensitivity of greater than 65%. Optionally, the memoryunit is configured to receive age information from said individual.Optionally, the computer-executable instructions factor in age of theindividual when assessing said advanced adenoma risk associated withsaid measurement of said panel of proteins.

Also provided herein are methods of assessing a colorectal health riskstatus in an individual. Also provided herein are ex vivo methods ofassessing a colorectal health risk status in a blood sample of anindividual. Some such methods comprise the steps of obtaining acirculating blood sample from the individual; obtaining a biomarkerpanel level for a biomarker panel comprising a list of proteins in thesample comprising AACT, CO3, CO9, MIF, PSGL, SEPR, CEA, CATD, CLUS,GDF15 and SAA1, and obtaining an age for the individual, wherein AACT,CO3, CO9, MIF, PSGL, SEPR, CEA, CATD, and age comprise colorectal cancerpanel information from said individual; and wherein CATD, CLUS, GDF15and SAA1 comprise advanced adenoma panel information from saidindividual; comparing said colorectal cancer panel information from saidindividual to a reference colorectal cancer panel information setcorresponding to a known colorectal cancer status; comparing saidadvanced adenoma panel information from said individual to a referenceadvanced adenoma panel information set corresponding to a known advancedadenoma status; and categorizing said individual as having a colorectalhealth risk if either of said colorectal cancer panel or said advancedadenoma panel does not differ significantly from a reference panelpositive for a colorectal health risk. Various aspects of these methodsare recited below, contemplated as distinct or in combination. Methodsherein are contemplated to include obtaining a circulating blood samplecomprises drawing blood from a vein or artery of the individual.Optionally, the panel information comprises age information for theindividual. Optionally, the list of proteins comprises no more than 20proteins. Optionally, the list of proteins comprises no more than 11proteins. Optionally, the categorizing has a sensitivity of at least 80%and a specificity of at least 50%. Optionally, the categorizing has asensitivity of at least 80% and a specificity of at least 47%.Optionally, the categorizing has a sensitivity of at least 83% and aspecificity of at least 47%. Optionally, methods herein comprisetransmitting a report of results of said categorizing to a healthcareprofessional. Optionally, the report indicates a sensitivity of at least8%. Optionally, the report indicates a specificity of at least 50%.Optionally, the report recommends that a colonoscopy be performed.Optionally, the individual undergoes a colonoscopy. Optionally, thereport recommends an independent surgical intervention. Optionally, theindividual undergoes an independent surgical intervention. Optionally,the report recommends undergoing an independent cancer assay.Optionally, the individual undergoes an independent cancer assay.Optionally, the report recommends undergoing a stool cancer assay.Optionally, the individual undergoes a stool cancer assay. Optionally,the report recommends administering an anticancer composition.Optionally, an anticancer composition is administered to the individual.Optionally, the report recommends continued monitoring. Methods are alsocontemplated herein wherein at least one parameter of said individual'sreference panel differs significantly from a corresponding value fromsaid reference panel set, and wherein said individual's reference panelinformation as a whole does not differ significantly from said referencepanel information set. Optionally, methods are contemplated wherein noparameter of said individual's reference panel information in isolationis indicative of said advanced adenoma status in said individual at asensitivity of greater than 65% or a specificity of greater than 65%.Optionally, the obtaining protein levels comprises contacting a fractionof the circulating blood sample to a set of antibodies, wherein the setof antibodies comprises antibodies specific to AACT, CO3, CO9, MIF,PSGL, SEPR, CEA, CATD, CLUS, GDF15 and SAA1. Optionally, the obtainingprotein levels comprises subjecting a fraction of the circulating bloodsample to a mass spectrometric analysis. Optionally, the obtainingprotein levels comprises contacting the sample to protein binding DNAaptamers. Optionally, the obtaining protein levels comprises contactingthe sample to an antibody array. Optionally, at least one of saidcomparing and said categorizing is performed on a computer configured toanalyze reference panel information. Optionally, said reference panelinformation set corresponding to a known advanced adenoma statuscomprises is a product of a machine learning model. Optionally, themachine learning model is trained using at least 100 biomarker panelscorresponding to known colorectal health status.

Provided herein are methods, compositions, kits, computer readablemedia, and systems for the diagnosis and/or treatment of at least one ofadvanced colorectal adenoma and colorectal cancer. Through the methodsand compositions provided herein, a sample is taken from an individualsuch as an individual at risk of advanced colorectal adenoma orcolorectal cancer. The sample is assayed to determine the accumulationlevels of a panel of markers such as proteins, for example a panel ofmarkers comprising or consisting of the markers in panels disclosedherein. In many cases the panels comprise proteins that individually areknown to play a role in indicating the presence of advanced colorectaladenoma or colorectal cancer, while in other cases the panels comprise aprotein or proteins not know to correlate with advanced colorectaladenoma or colorectal cancer. However, in all cases the identificationand accumulation of markers into a panel results in a level ofspecificity, sensitivity or specificity and sensitivity thatsubstantially surpasses that of individual markers or smaller or lessaccurate sets of markers.

Additionally, methods, panels and other tests disclosed hereinsubstantially surpass the sensitivity, specificity, or sensitivity andspecificity of currently available tests such as currently availableblood-based tests. Panel accumulation levels are measured in a number ofways in various embodiments, for example through an ELISA assay, throughmass spectroscopy analysis or through alternate approaches to proteinaccumulation level quantification.

Panel accumulation levels are compared to a positive control or negativecontrol standard, or to a model of advanced colorectal adenoma orcolorectal cancer accumulation levels or of healthy accumulation levels,such that a prediction is made regarding an assayed individual's healthstatus. In some cases, a panel assay result is accompanied by arecommendation regarding an intervention or an alternate verification ofthe panel assay results.

Provided herein are biomarker panels and assays useful for the diagnosisand/or treatment of at least one of advanced colorectal adenoma andcolorectal cancer.

Also provided herein are kits, comprising a computer readable mediumdescribed herein, and instructions for use of the computer readablemedium.

A number of treatment regimens are contemplated herein and known to oneof skill in the art, such as chemotherapy, administration of a biologictherapeutic agent, and surgical intervention such as low anteriorresection or abdominoperineal resection, or ostomy.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 depicts a Biomarker Panel development pipeline.

FIG. 2 illustrates an AUC curve for a lead CRC panel.

FIG. 3 illustrates an AUC curve for a lead AA panel.

FIG. 4 presents validation data for a lead CRC panel.

FIG. 5 presents protein levels from biomarker proteins in CRC andhealthy control samples.

FIG. 6 presents protein levels from biomarker proteins in AA and healthycontrol samples.

FIG. 7A illustrates a Discovery ROC AUC plot for CRC Model 1.

FIG. 7B illustrates a Validation ROC AUC plot for CRC Model 1.

FIG. 8A illustrates a Discovery ROC AUC plot for CRC Model 2.

FIG. 8B illustrates a Validation ROC AUC plot for CRC Model 2.

FIG. 9A illustrates a Discovery ROC AUC plot for CRC Model 3.

FIG. 9B illustrates a Validation ROC AUC plot for CRC Model 3.

FIG. 10A illustrates a Discovery ROC AUC plot for CRC Model 4.

FIG. 10B illustrates a Validation ROC AUC plot for CRC Model 4.

FIG. 11A illustrates a Discovery ROC AUC plot for CRC Model 5.

FIG. 11B illustrates a Validation ROC AUC plot for CRC Model 5.

FIG. 12A illustrates a Discovery ROC AUC plot for CRC Model 6.

FIG. 12B illustrates a Validation ROC AUC plot for CRC Model 6.

FIG. 13A illustrates a Discovery ROC AUC plot for CRC Model 7.

FIG. 13B illustrates a Validation ROC AUC plot for CRC Model 7.

FIG. 14A illustrates a Discovery ROC AUC plot for CRC Model 8.

FIG. 14B illustrates a Validation ROC AUC plot for CRC Model 8.

FIG. 15A illustrates a Discovery ROC AUC plot for CRC Model 9.

FIG. 15B illustrates a Validation ROC AUC plot for CRC Model 9.

FIG. 16A illustrates a Discovery ROC AUC plot for CRC Model 10.

FIG. 16B illustrates a Validation ROC AUC plot for CRC Model 10.

FIG. 17A illustrates a Discovery ROC AUC plot for CRC Model 5 with NOC.

FIG. 17B illustrates a Validation ROC AUC plot for CRC Model 5 with NOC.

FIG. 18 illustrates a Max Accuracy plot for CRC Models 1-10.

FIG. 19 depicts a Computer System architecture consistent with theMethods, Compositions, Kits and Systems disclosed herein.

FIG. 20 presents AUC values for randomly generated CRC panels from atargeted-MS enriched biomarker population.

DETAILED DESCRIPTION

Provided herein are biomarker panels, methods, compositions, kits, andsystems for the non-invasive assessment of colorectal health, forexample through the detection of at least one of advanced colorectaladenoma (“AA”) and colorectal cancer (“CRC”). Biomarker panels, methods,compositions, kits, and systems described herein are used to determine alikelihood that a subject has a colorectal condition such as at leastone of an advanced colorectal adenoma and CRC through the noninvasiveassay of a sample taken from circulating blood circulating blood. Somesuch biomarker panels are used noninvasively to detect a colorectalhealth issue such as colorectal cancer with a sensitivity of as much as81% or greater, and a specificity of as much as 78% or greater. Anexemplary CRC biomarker panel comprises the markers AACT, CATD, CEA,CO3, CO9, MIF, PSGL, and SEPR, and the non-protein biomarker of age ofthe individual providing the sample. Some such biomarker panels are usednoninvasively to detect a colorectal health issue such as an advancedadenoma with a sensitivity of as much as 50% or greater, and aspecificity of as much as 80% or greater. An exemplary biomarker panelrelevant to advanced adenoma assessment comprises the markers CATD,CLUS, GDF15 and SAA1.

Biomarker panels as disclosed herein share a property that sensitive,specific conclusions regarding an individual's colorectal health aremade using protein level information derived from circulating blood,alone or in combination with other information such as an individual'sage, gender, health history or other characteristics. A benefit of thepresent protein panels is that they provide a sensitive, specificcolorectal health assessment using conveniently, noninvasively obtainedsamples. There is no need to rely upon data obtained from an intrusiveabdominal assay such as a colonoscopy or a sigmoidoscopy, or from stoolsample material. As a result compliance rates are substantially higher,and colorectal health issues are more easily recognized early in theirprogression, so that they may be more efficiently treated. Ultimately,the effect of this benefit is measured in lives saved, and issubstantial.

Biomarker panels as disclosed herein are selected such that theirpredictive value as panels is substantially greater than the predictivevalue of their individual members. Panel members generally do notco-vary with one another, such that panel members provide independentcontributions to the panel's overall health signal. Accordingly, a panelis able to substantially outperform the performance of any individualconstituent indicative of an individual's colorectal health status, suchthat a commercially and medicinally relevant degree of confidence (suchas sensitivity, specificity or sensitivity and specificity) is obtained.Thus, in the panels as disclosed herein, multiple panel membersindicative of a health issue provide a much stronger signal than isfound, for example in a panel wherein two or more members rise or fallin strict concert such that the signal derived therefrom is effectivelya single signal, repeated twice. Accordingly, panels as disclosed hereinare robust to variation in single constituent measurements. For examplebecause panel members vary independently of one another, panels hereinoften indicate a health risk despite the fact that one or more than oneindividual members of the panel would not indicate that the health riskis present if measured alone. In some cases, panels herein indicate ahealth risk at a significant level of confidence despite the fact thatno individual panel member indicates the health risk at a significantlevel of confidence on its own. In some cases, panels herein indicate ahealth risk at a significant level of confidence despite the fact thatat least one individual member indicates at a significant level ofconfidence that the health risk is not present.

Biomarkers consistent with the panels herein comprise biologicalmolecules that circulate in the bloodstream of an individual, such asproteins. Readily available information such as individual's age,gender, weight, height, body mass index or other easily measured orobtained information is also eligible as a marker in some cases. Inparticular, some panels herein rely upon age, gender, or age and genderas biomarkers.

Common to many biomarkers herein is the ease with which they are assayedin an individual. Biomarkers herein are readily obtained by a blood drawfrom an artery or vein of an individual, or are obtained via interviewor by simple biometric analysis. A benefit of the ease with whichbiomarkers herein are obtained is that invasive assays such ascolonoscopy or sigmoidoscopy are not required for biomarker measurement.Similarly, stool samples are not required for biomarker determination.As a result, panel information as disclosed herein is often readilyobtained through a blood draw in combination with a visit to a doctor'soffice. Compliance rates are accordingly substantially higher than arecompliance rates for colorectal health assays involving stool samples orinvasive procedures.

Exemplary panels disclosed herein comprise circulating proteins orfragments thereof that are recognizably or uniquely mapped to theirparent protein, and in some cases comprise a readily obtained biomarkersuch as an individual's age.

Characteristics of Panels Disclosed Herein relative to other BiomarkerPanels

Panels disclosed herein substantially outperform individual markers orrandomly generated panels. Although at least some members of the panelsherein are implicated in cancer, the panels herein far outperform panelsderived randomly from any art teachings. This is illustrated byexamination of panel performance as compared to individual members,randomly generated panels, and in light of the unpredictability ofindividual markers for any individual health assessment.

Panels were constructed from an original candidate pool of 187 potentialbiomarkers selected from the literature. See FIG. 1. Using a 274 memberage and gender matched discovery sample set, targeted mass spectroscopywas used to identify 28 biomarkers from the original set that co-varywith health status of the 274 members of the discovery sample set. This28 member set is not a random selection of the 187 member originalcandidate pool, and the 28 member set was not selected from the original187 member candidate pool based upon any teaching in the art.

The 28 member set was tested against a separate age and gender matched300 member sample set to come to CRC panels as disclosed herein, such asthe 8 member panel comprising AACT, CO3, CO9, MIF, PSGL, CATD, CEA andSEPR. This and similar panels were selected from an original 187 membercandidate pool but are not taught to be particularly effective incombination to the exclusion of other candidate pool constituents.Rather, the panel is come to through repeated analysis of independentlyderived samples in combination with the inventor's own insights intopanel construction and health status prediction.

FIG. 2 depicts an AUC plot for a lead CRC panel derived herein. The AUCplot clearly illustrates that the CRC panel performs substantiallybetter than random chance, as depicted by the diagonal on this figure.FIG. 3 depicts an AUC plot for a lead AA panel derived herein. The AUCplot clearly illustrates that the AA panel performs substantially betterthan random chance, as depicted by the diagonal on this figure.

Biomarker panels herein perform substantially better than any randomselection of biomarkers individually implicated in cancer generally,such as those of the 187 member candidate pool. That is, if one of skillin the art were to start with a list of biomarkers available in theliterature and randomly assemble, or even assemble in light of teachingsavailable to one of skill in the art, a biomarker panel to use to assayfor a colorectal health issue such as colorectal cancer or advancedadenoma in an individual, one does not come to a biomarker as disclosedherein. Biomarker panels disclosed herein substantially outperformrandomly selected panels and panels selected in light of the art.

Biomarker panels herein perform substantially better than any individualconstituent marker individually implicated in cancer generally, such asthose of the 187 member candidate pool. Some individual biomarkersindicate CRC or advanced adenoma, but with a sensitivity and aspecificity that is far below that of the biomarker panels as disclosedherein. Use of individual biomarkers, or combinations of biomarkers notrecited or readily apparent to one of skill in the art from thedisclosure herein, is not contemplated pursuant to this disclosure.

Reference is made to FIGS. 5 and 6. In these figures, individual proteinlevels are compared between samples that are positive or negative forCRC (FIG. 5) or AA (FIG. 6). Proteins presented in these figures are notrandomly selected, rather they are chosen from the MS-enriched set of 28proteins identified from among the 187 protein list identified in theart as being potentially of relevance to cancer health assessment. Foreach paired boxplot, the healthy sample levels are at left or top, whilethe CRC or AA positive protein levels are depicted at right or bottom.For the vast majority of individual protein markers, there is littledifference between the condition positive and condition negative proteinlevels. Levels are not identical, but the difference in most cases doesnot look to one of skill in the art to be significant, particularly at alevel at which one would base a colorectal health assessment. With a fewexceptions, such as FIG. 5 CEA, CRP, or GARS levels, the listed proteinlevels are quite similar between condition and no-condition samples.See, as representative examples FIG. 5 A1AG1, A1AT, AACT, ANAX1, APOA1,CAH1, CO9, GELS, HTP, OSTP or PSGL, among others. The situation for FIG.6 is quite similar, with individual protein levels rarely differing veryconspicuously between condition positive and condition negativeindividuals.

It is clear from FIG. 5 and FIG. 6 that no individual marker, even fromthis targeted-MS enriched set, is expected to perform as well as thepanels presented herein. Furthermore, there is little suggestion fromthe protein levels presented in FIG. 5 or FIG. 6 that combinations ofprotein levels may have a synergistic effect so as to attain theperformance of the panels as disclosed herein.

Aggregation of protein markers alone does not accomplish the level ofperformance of the panels disclosed herein. Reference is made to Example21, below. Random panels are generated from the targeted MS-enriched setof 28 markers, and their performance is compared to that of the panelsherein. The enriched 28 member set is already expected to yield panelsthat perform much better than those generated from the unenriched parent187 marker set. It is observed that the panels herein, particularly thepanels of 8-10 members, as shown, substantially outperform panelsgenerated at random from an already enriched set of protein markers.These random panels do not represent panels that one would come to fromthe art, as they are already enriched from the 187 member list asmentioned in the art as being relevant to cancer detection. Thus, evenperformance comparable to levels seen in the randomly generated panelsfrom the 28 marker set represents a substantial improvement over moregenerally apparent panels. Panels herein, however generally match (AAlead panel) or more often substantially outperform (CRC panels) up toalmost 100% of the randomly generated panels from the enriched set of 28markers. See again Example 21.

Biomarker panels herein yield results that are more reliable, moresensitive and more specific than simply the collection of theirindividual constituents. That is, in some cases individual biomarkersare detected at levels that are individually not informative with adegree of sensitivity and specificity to be medically relevant, but thelevel of the biomarker panel nonetheless provides a colorectal healthassessment with a degree of confidence that is medically actionable. Insome cases no individual biomarker of the panel is present at a levelthat is individually indicative of a health issue warranting follow-up,but the biomarker panel as a whole, assessed as indicated herein,provides an assessment that is indicative of a health issue warrantingfollow-up.

Biomarkers herein yield results that are in some cases qualitativelydifferent from those of their constituent biomarkers. That is, in somecases one or more individual biomarkers of the panel are present at alevel that is individually indicative of a colorectal health status thatis contradictory to the health status indicated by the level of thepanel as a whole, including the contradictory biomarker. In such cases,it is often found that independent health assessment, for example bycolonoscopy or by stool sample analysis, supports the panel assessmentrather than the health status assessment provided by the contradictoryindividual marker.

Reference is made to Example 22 below. In that example the CRC biomarkerpanels provide predictions that are inconsistent with the predictionsthat result from looking at constituent biomarker levels in isolation.The protein CO3, in particular, is measured at a level in theCRC-positive individual, patient 1, that is intermediate between the CO3levels observed for two CRC-negative individuals. If one were scoringthese biomarkers individually rather than as parts of a panel, one wouldbe unlikely to score patient 1 as CRC positive and patients 2 and 3 asCRC negative in light of patient 1's CO3 level falling between those ofpatient 2 and 3.

However, using the panel analysis as disclosed herein, one comes to aresult that is qualitatively different from the result expected byexamination of an individual panel biomarker in isolation. This data aspresented in Example 22, below, highlights the fact that the panelsherein are not simply quantitatively better but are also in some casesqualitatively different from their individual biomarker constituents.

Accordingly, biomarker panels disclosed herein are understood to performbetter than a random collection of candidate markers as taught by theliterature. Biomarker panels disclosed herein are also understood toperform better statistically, and in some cases qualitativelydifferently, than do their individual biomarker constituents, such thata health assessment from the biomarker panel as a whole is either moreaccurate or in some cases provides a result that is qualitativelydifferent from that of one or more individual biomarker constituents.

Panel Constituents

Some biomarker panels comprise some or all of the protein markersrecited herein, subsets thereof or listed markers in combination withadditional markers or biological parameters. A lead biomarker panelrelevant to colorectal cancer assessment comprises at least 4 markers,up to the full list, alone or in combination with additional markers,said list selected from the following: AACT, CATD, CEA, CO3, CO9, MIF,PSGL, SEPR, and also including age as a biomarker. A lead biomarkerpanel relevant to advanced adenoma assessment comprises markers selectedfrom the following: CATD, CLUS, GDF15 and SAA1. A lead biomarker panel,or a combination of biomarker panels having combined colorectal cancerand advanced adenoma assessment capabilities comprises biomarkers suchas AACT, CEA, CO3, CO9, MIF, PSGL, SEPR, CATD, CLUS, GDF15 and SAA1, andage as a non-protein biomarker, or a subset thereof optionally having atleast one individual marker excluded or replaced with one or moremarkers.

Often, it is convenient or efficient to combine a colorectal cancerbiomarker panel and an advanced adenoma panel into a single kit or asingle biomarker panel. In these cases, one sees a kit comprising elevenbiomarkers, or a subset or larger set thereof, including AACT, CATD,CEA, CO3, CO9, MIF, PSGL, SEPR, CLUS, GDF15 and SAA1, of which AACT,CEA, CO3, CO9, MIF, PSGL, and SEPR or a subset or larger groupcomprising these markers is informative as to colorectal cancer status;CLUS, GDF15 and SAA1 or a subset or larger group comprising thesemarkers is informative as to advanced adenoma status; and CATD, ifincluded, is informative as to both colorectal cancer status andadvanced adenoma status.

Alternate colorectal cancer biomarker panels are listed below. Much likethe panel discussed above, these panels, or subsets or additions, areused alone or in combination with the above-mentioned advanced adenomapanel, optionally using markers such as CATD, CLUS, GDF15 or SAA1 to beindicative of advanced adenoma and colorectal cancer. An exemplarybiomarker panel comprises at least 4 markers, up to the full list, aloneor in combination with additional markers, said list selected from thefollowing: A1AG1, A1AT, CATD, CEA, CO9, OSTPxAge, SEPR, wherein OSTPxAgerefers to OSTP viewed in combination with individual age. An exemplarybiomarker panel comprises at least 4 markers, up to the full list, aloneor in combination with additional markers, said list selected from thefollowing: A1AG1, A1AT, APOA1, CATD, CEA, CLUS, CO3, CO9, FGB, FIBG,GARS, GELS, MIF, PRDX1, PSGL, SBP1, SEPR. An exemplary biomarker panelcomprises at least 4 markers, up to the full list, alone or incombination with additional markers, said list selected from thefollowing: A1AG1, A1AT, CATD, CEA, CO9, GARS, SEPR. An exemplarybiomarker panel comprises at least 4 markers, up to the full list, aloneor in combination with additional markers, said list selected from thefollowing: A1AG1, A1AT, AACT, CATD, CEA, CO9, CRP, AACT, CO9, CRP, CRP,CRP, CRP, CRP, CRP, GELS, S10A8, S10A8, S10A8, S10A8, S10A9, S10A9,GARS, SAA1, SEPR. An exemplary biomarker panel comprises at least 4markers, up to the full list, alone or in combination with additionalmarkers, said list selected from the following: CATD, CEA, CO3, CO9,GARS, GELS, SEPR, TFRC. An exemplary biomarker panel comprises at least4 markers, up to the full list, alone or in combination with additionalmarkers, said list selected from the following: CATD, CEA, AACT, CO9,SEPR. An exemplary biomarker panel comprises at least 4 markers, up tothe full list, alone or in combination with additional markers, saidlist selected from the following: A1AT, C3218600, C387796, C597612,C979276, CATD, CEA, GARS, GELS, SEPR. An exemplary biomarker panelcomprises at least 4 markers, up to the full list, alone or incombination with additional markers, said list selected from thefollowing: A1AG1, A1AT, CATD, CEA, CO9, SEPR, CATD/SEPR, CATD/GELS,CO9/SEPR, A1AT/FIBG, wherein a “I” indicates that a biomarker comprisesa ratio of one protein or other biomarker level to a second protein orother biomarker level. An exemplary biomarker panel comprises at least 4markers, up to the full list, alone or in combination with additionalmarkers, said list selected from the following: CATD, CEA, CO3, CO9,S10A8, GELS, SEPR, TFRC. An exemplary biomarker panel comprises at least4 markers, up to the full list, alone or in combination with additionalmarkers, said list selected from the following: A1AG1, CATD, CEA, CO3,CO9, GELS, SEPR. For biomarker panels disclosed herein, variants havingall but 1, 2, 3, or about 90%, 80%, 70%, 60%, or 50% of the biomarkersrecited are also contemplated, as are panels that comprise additionalbiomarkers or control markers.

Biomarkers are measured through a number of approaches consistent withthe disclosure herein. In many cases biomarkers are measured through animmunological interaction, such as that which occurs in an ELISA assaythrough which proteins or protein fragments in a blood sample from anindividual are bound to specific antibodies, and the extent of bindingis quantified as a measure of protein abundance in the sample. ELISAassays capable of measuring biomarker panels as disclosed herein arecontemplated as embodiments of the present disclosure as kits.

Alternately or in combination, biomarkers are measured through massspectrometric methods such as MS, MS/MS, MALDI-TOF or other massspectrometric approaches as appropriate. Often, the MS approachquantifies a fragment of a biomarker rather than the full-lengthprotein. However, such approaches are sufficient to determine theprotein level of the biomarker to an accuracy sufficient for acolorectal health assessment as disclosed herein.

Some details of panel performance is dependent upon assay approach, suchthat some panels perform slightly better using an immunological or amass spectrometric approach. However, it is observed that in many casespanel performance is largely independent of assay method, such that apanel that performs slightly better using an immunological assay isnonetheless informative as to an individual's colorectal health statuswhen assayed using mass spectrometric analysis, or vice versa.

Once an expression level for a biomarker panel is determined, acolorectal health assessment is available for the individual from whichthe sample is obtained. A number of approaches are available to one ofskill in the art to generate or come to a colorectal health assessmentfrom an individual's biomarker panel expression level.

Some assessments rely upon comparison of an individual's biomarker panellevel to a reference level, such as a reference biomarker panel levelfrom an individual known or independently verified to be in goodcolorectal health, or from an individual known or independently verifiedto be in poor colorectal health, such as is the case for an individualhaving colorectal cancer or at least one advanced adenoma. Alternatelyan individual's biomarker panel level is compared to a reference levelconstructed from a plurality of individuals of common known colorectalhealth status. In some cases the reference is an average of known panellevels from a plurality of individuals, or alternately is a rangedefined by the range of panel levels observed in the referenceindividuals. A range reference panel level is in some cases a weightedrange, such that outlier values among the individuals having a commoncolorectal health status are given lower predictive value than panellevels that are common to a plurality or majority or all of the panellevels.

In more complex assessment approaches, an individual's biomarker panellevel is compared to a reference level constructed from a larger numberof individuals of common known colorectal health status, such as atleast 10, at least 50, at least 100, at least 500, at least 1000 or moreindividuals. Often, the reference individuals are evenly distributed inhealth status between positive and negative for a colorectal healthstatus such as positive and negative for colorectal cancer, or positiveand negative for advanced adenoma. Assessment comprises in some casesiterative or simultaneous comparison of an individual's biomarker panellevel to a plurality of references of known health status.

Alternately or in combination, a plurality of known reference biomarkerpanel levels are used to train a computational assessment algorithm suchas a machine learning model such that a single comparison between anindividual's biomarker panel level and a reference provides an outcomethat integrates or aggregates information from a large number ofindividuals of common known colorectal health status, such as at least10, at least 50, at least 100, at least 500, at least 1000 or moreindividuals. Generation of such a reference often facilitates muchfaster assessment of an individual's colorectal health status, orassessment using much less computational power.

A reference is generated from a plurality of reference individualbiomarker levels through any of a number of computational approachesknown to one of skill in the art. Machine learning models are readilyconstructed, for example, using any number of statistical programmingprogramming languages such as R, scripting languages such as Python andassociated machine learning packages, data mining software such as Wekaor Java, Mathematica, Matlab or SAS.

An individual's biomarker panel level is compared to a reference asgenerated above or otherwise by one of skill in the art, and an outputassessment is generated. A number of output assessments are consistentwith the disclosure herein. Output assessments comprise a singleassessment, often narrowed by a sensitivity, specificity or sensitivityand specificity parameter, indicating a colorectal health statusassessment. Alternately or in combination, additional parameters areprovided, such as an odds ratio indicative of the relative increase inchance of suffering from a colorectal health issue in light of theindividual's biomarker panel level or biomarker panel level assessment.

Results are variously provided to the individual or to a health careprofessional or other professional. Results are optionally accompaniedby a heath recommendation, such as a recommendation to confirm orindependently assess a colorectal health status assessment, for exampleusing a stool sample assay or an invasive approach such as acolonoscopy, sigmoidoscopy or other supplemental assay for colorectalhealth.

A recommendation optionally includes information relevant to a treatmentregimen, such as information indicating that a treatment regimen such asa polypectomy, radiotherapy, chemotherapy, antibody therapy, biosimilartreatment or other treatment regimen, such as information indicative ofsuccess or efficacy of the regimen. Efficacy of a regimen is assessed insome cases by comparison of an individual's biomarker panel level at afirst time point, optionally prior to a treatment and a later secondtime point, optionally subsequent to a treatment instance. Biomarkerpanel levels are compared to one another, each to a reference, orotherwise assessed so as to determine whether a treatment regimendemonstrates efficacy such that it should be continued, increased,replaced with an alternate regimen or discontinued because of itssuccess in addressing the colorectal health issue such as colorectalcancer or advanced adenoma. Some assessments rely upon comparison of anindividual's biomarker panel level at multiple time points, such as atleast one time point prior to a treatment and at least one time pointfollowing a treatment. Biomarker panel levels are compared one toanother or to at least one reference biomarker panel level or both toone another and to at least one reference biomarker panel level.

Health Assessment Assays

The biomarker panels, methods, compositions, and kits described hereinprovide assays for at least one of advanced colorectal adenoma and CRCbased on detection or measurement of biomarkers in a biological sampleobtained from a subject. The biological sample preferably is a bloodsample drawn from an artery or vein of an individual. The blood samplecan be a whole blood sample, a plasma sample, or a serum sample. Thedisclosure provided herein detects at least one of advanced colorectaladenoma and CRC from a sample such as a blood sample with a sensitivityand a specificity that renders the outcome of the test reliable enoughto be medically actionable. Health assessment methods, systems, kits andpanels herein have at least one of a sensitivity of at least 70% andspecificity of at least 70%. Such methods can have at least one of asensitivity of 70% or greater and specificity of at least 70% based onmeasurement of 15 or fewer biomarkers in the biological sample. In somecases, a method provided herein detects at least one of advancedcolorectal adenoma and CRC. Such method can have at least one of asensitivity at least 70% and specificity at least 70% based onmeasurement of no more than 4 biomarkers, 5 biomarkers, 6 biomarkers, 7,biomarkers, 8 biomarkers, 9 biomarkers, 10 biomarkers, 11, biomarkers,12 biomarkers, 13 biomarkers, 14 biomarkers, or 15 biomarkers. Somepreferred embodiments allow one to assess colorectal cancer using abiomarker panel of 8 markers. Some preferred embodiments allow one toassess advanced adenoma using a panel of 4 biomarkers. Some biomarkerpanels allow one to assess both colorectal cancer and advanced adenomausing a combined panel of 11 biomarkers.

In some cases the biomarker panels, methods, compositions, and kitsdescribed herein are useful to screen for individuals at elevated riskfor CRC or advanced adenoma. In some cases, a positive detection of atleast one of an advanced colorectal adenoma and CRC based upon a methoddescribed herein is used to identify patients for whom to recommend anadditional diagnostic method. For example, in some cases where a methodherein yields a positive result, such method is used to alert acaregiver to perform an additional test such as a colonoscopy, asigmoidoscopy, an independent cancer assay, or a stool cancer assay.

The biomarker panels, methods, compositions, and kits described hereinare also useful as a quality control metric for a colonoscopy,sigmoidoscopy, or colon tissue biopsy. For example, a positive detectionof at least one of an advanced colorectal adenoma and CRC based upon amethod described herein can be used to validate a result of acolonoscopy, sigmoidoscopy, or colon tissue biopsy. For example, in somecases wherein a colonoscopy, sigmoidoscopy, or colon tissue biopsyyielded a negative result, but a method described herein yielded apositive result, such method can be used to alert a caregiver to performanother colonoscopy, sigmoidoscopy, or colon tissue biopsy, or toinitiate a treatment regimen such as administration of a pharmaceuticalcomposition.

Some methods provided herein comprise (a) obtaining a biological samplefrom a subject; (b) measuring a panel of biomarkers in the biologicalsample of the subject; (c) detecting a presence or absence of at leastone of advanced colorectal adenoma and CRC in the subject based upon themeasuring; and (d) either (i) treating the at least one of advancedcolorectal adenoma CRC and in the subject based upon the detecting, or(ii) recommending to the subject a colonoscopy, sigmoidoscopy, orcolorectal tissue biopsy based upon the results of the detecting. Forthe purposes of one or more methods described herein, “treating”comprises providing a written report to the subject or to a caretaker ofthe subject which includes a recommendation to initiate a treatment forthe CRC. For the purposes of one or more methods described herein,“recommending to the subject a colonoscopy” comprises providing awritten report to the subject or to a caretaker of the subject whichincludes a recommendation that the subject undergo a colonoscopy,sigmoidoscopy, or tissue biopsy to confirm an assessment of the CRC. Insome cases, the colonoscopy, sigmoidoscopy, or tissue biopsy can be usedto remove the at least one of advanced colorectal adenoma and CRC,thereby treating the at least one of advanced colorectal adenoma andCRC.

Exemplary methods optionally comprise (a) obtaining data comprising ameasurement of a biomarker panel in a biological sample obtained from asubject, (b) generating a subject-specific profile of the biomarkerpanel based upon the measurement data, (c) comparing thesubject-specific profile of the biomarker panel to a reference profileof the biomarker panel; and (d) determining a likelihood of at least oneof advanced colorectal adenoma and colorectal cancer based upon (c).

Exemplary methods optionally comprise (a) measuring a biomarker panel ina biological sample obtained from the subject; (b) detecting a presenceor absence of colorectal cancer and/or advanced colorectal adenoma inthe subject based upon the measuring; and (c) treating the colorectalcancer in the subject based upon the detecting.

Exemplary methods optionally comprise (a) obtaining data comprising ameasurement of a biomarker panel in a biological sample obtained from asubject, (b) generating a subject-specific profile of the biomarkerpanel based upon the measurement data, (c) comparing thesubject-specific profile of the biomarker panel to a reference profileof the biomarker panel; and (d) determining a likelihood of at least oneof advanced colorectal adenoma and colorectal cancer based upon (c).Some methods provided herein comprise (a) measuring a biomarker panel ina biological sample obtained from the subject; (b) detecting a presenceor absence of colorectal cancer and/or advanced colorectal adenoma inthe subject based upon the measuring; and (c) recommending to thesubject at least one of a colonoscopy, sigmoidoscopy, and tissue biopsyin the subject based upon the detecting. Exemplary methods optionallycomprise diagnosis of colorectal cancer or monitoring colorectal cancer,so as to establish a prognosis for the subject. The levels of one or acombination of the proteins listed can over time be linked todifferential outcomes for cancer patients, possibly depending on thetreatment chosen. Exemplary methods optionally comprise monitoring theprogression of cancer in a subject by comparing the accumulation levelsof one or more biomarkers in a sample from a subject to the accumulationlevels of the one or more biomarkers in a sample obtained from thesubject at a subsequent point in time, wherein a difference in theexpression of said one or more biomarkers diagnoses or aids in thediagnosis of the progression of the cancer in the subject. Someexemplary methods comprise monitoring the effectiveness of a treatment.In some cases, a method for monitoring the effectiveness of a treatmentcomprises comparing the accumulation levels of one or more biomarkers ina sample from a subject prior to providing at least a portion of atreatment to the accumulation levels of said one or more biomarkers in asample obtained from the subject after the subject has received at leasta portion of the treatment, and wherein a difference in the accumulationlevels of said one or more biomarker diagnoses or aids in the diagnosisof the efficacy of the treatment.

Biomarkers

In some cases, biomarker panels described herein comprise at least twobiomarkers. The biomarkers can be selected from the group comprisingA1AG1, A1AT, AACT, APOA1, CATD, CEA, CLUS, CO3, CO9, CRP, FGB, FIBG,GARS, GELS, HPT, MIF, OSTP, PRDX1, PSGL, S10A8, S10A9, SAA1, SBP1, SEPR,and TFRC, or fragments thereof. Any of the biomarkers described hereincan be protein biomarkers. Furthermore, the group of biomarkers in thisexample can in some cases additionally comprise polypeptides with thecharacteristics found in Table 1.

Exemplary protein biomarkers and, when available, their human amino acidsequences, are listed in Table 1, below. Protein biomarkers comprisefull length molecules of the polypeptide sequences of Table 1, as wellas uniquely identifiable fragments of the polypeptide sequences ofTable 1. Markers can be but do not need to be full length to beinformative. In many cases, so long as a fragment is uniquelyidentifiable as being derived from or representing a polypeptide ofTable 1, it is informative for purposes herein.

In some embodiments a panel of biomarkers may comprise a panel ofproteins Disclosed herein are panels of proteins suitable for CRC or AAdetection. In some cases, panels of proteins described herein compriseat least two proteins. In some cases, the proteins is selected from thegroup consisting of AACT, CATD, CEA, CO3, CO9, MIF, PSGL, SEPR, CLUS,GDF15, and SAA1 or fragments thereof. In some cases the panel is a CRCpanel, and the proteins tested comprise AACT, CATD, CEA, CO3, CO9, MIF,PSGL, and SEPR. In some cases, the biomarker panel comprises AACT, CATD,CEA, CO3, CO9, MIF, PSGL, and SEPR and the age of the subject. In somecases, the ratio of one or more pairs of protein accumulation levels isused to categorize a patients CRC status. For example, in some cases thecategorizing comprises comparing ratios of CATD/SEPR, CATD/CO3,CO9/SEPR., and/or A.1AT/GDF15. In some cases, the subject's age isincluded for evaluation in addition to the protein accumulation levels.In some cases, the protein panel comprises AACT, CATD, CEA, CO3, CO9,MIF, PSGL, and SEPR, and the sensitivity for CRC detection is at least50%, at least 55%, at least 60%, at least 65%, at least 70%, at least75%, at least 80%, at least 85%, at least 90%, at least 95%, at least96%, at least 97%, at least 98%, at least 99%, or about 100%.

In some cases, the protein panel comprises AACT, CATD, CE.A, CO3, CO9,MIF, PSGL, and SEPR, and the sensitivity for CRC detection is at least81%. In some cases, the protein panel comprises AACT, LAID, CEA, CO3,CO9, MIF, PSGL, and SEPR, and the specificity for CRC detection is atleast 50%, at least 55%, at least 60%, at least 65%, at least 70%, atleast 75%, at least 80%, at least 85%, at least 90%, at least 95%, atleast 96%, at least 97%, at least 98%, at least 99%, or about 100%. Insome cases, the protein panel comprises AACT, CATD, CEA, CO3, CO9, MIF,PSGL, and SEPR, and the specificity for CRC detection is at least 78%.In some cases, the protein panel comprises AACT, CATD, CEA, CO3, CO9,MIF, PSGL, and SEPR, and the sensitivity for CRC detection is at least81% and the specificity is 78%. Furthermore, in some cases the panel ofproteins in these examples additionally comprises polypeptides with thecharacteristics found in Table 1. In some cases, the biomarker panelcomprises AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR, and the age ofthe subject, and the sensitivity for CRC detection is at least 50%, atleast 55%, at least 60%, at least 65%, at least 70%, at least 75%, atleast 80%, at least 85%, at least 90%, at least 95%, at least 96%, atleast 97%, at least 98%, at least 99%, or about 100%. In some cases, thebiomarker panel comprises AACT, CATD, CEA, CO3, CO9, MIF, PSGL, andSEPR, and the age of the subject, and the sensitivity for CRC detectionis at least 81%. In some cases, the biomarker panel comprises AACT,CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR, and the age of the subject,and the specificity for CRC detection is at least 50%, at least 55%, atleast 60%, at least 65%, at least 70%, at least 75%, at least 80%, atleast 85%, at least 90%, at least 95%, at least 96%, at least 97%, atleast 98%, at least 99%, or about 100%. In some cases, the biomarkerpanel comprises AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR, and theage of the subject, and the specificity for CR.0 detection is at least78%. In some cases, the biomarker panel comprises AACT, CATD, CEA, CO3,CO9, MIF, PSGL, and SEPR, and the age of the subject, and thesensitivity for CRC detection is at least 81% and the specificity is78%. In some cases, the protein panel comprises AACT, CATD, CEA, CO3,CO9, MIF, PSGL, and SEPR, and the positive predictive value for CRCdetection is at least 50%, at least 55%, at least 60%, at least 65%, atleast 70%, at least 75%, at least 80%, at least 85%, at least 90%, atleast 95%, at least 96%, at least 97%, at least 98%, at least 99%, orabout 100%. In some cases, the protein panel comprises AACT, CATD, CEA,CO3, CO9, M PSGL, and SEPR, and positive predictive value is 31%. Insome cases, the protein panel comprises AACT, CATD, CEA, CO3, CO9, MIF,PSGL, and SEPR, and the age of the subject, and the positive predictivevalue for CR.0 detection is at least 50%, at least 55%, at least 60%, atleast 65%, at least 70%, at least 75%, at least 80%, at least 85%, atleast 90%, at least 95%, at least 96%, at least 97%, at least 98%, atleast 99%, or about 100%. In some cases, the protein panel comprisesAACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR, and the age of thesubject, and positive predictive value is 31%. In some cases, thebiomarker panel comprises AACT, CATD, CEA, CO3, CO9, MIF, PSGL, andSEPR, and the age of the subject, and the sensitivity for CRC detectionis at least 81%, the specificity is 78%, and the positive predictivevalue is 31%. In some cases, the biomarker panel comprises AACT, LAID,CEA, CO3, CO9, MW, PSGL, and SEPR, and the sensitivity for CRC detectionis at least 81%, the specificity is 78%, and the positive predictivevalue is 31%. Furthermore, in some cases the panel of proteins in theseexamples additionally comprises polypeptides with the characteristicsfound in Table 1.

TABLE 1 Biomarkers and corresponding protein sequences Protein NameSymbol Sequence Alpha-1-acid A1AG1MALSWVLTVLSLLPLLEAQIPLCANLVPVPITNATLDQITGKWFY glycoprotein 1IASAFRNEEYNKSVQEIQATFFYFTPNKTEDTIFLREYQTRQDQCIYNTTYLNVQRENGTISRYVGGQEHFAHLLILRDTKTYMLAFDVNDEKNWGLSVYADKPETTKEQLGEFYEALDCLRIPKSDVVYTDW KKDKCEPLEKQHEKERKQEEGES(SEQ ID NO: 1) Alpha-1 A1AT MPSSVSWGILLLAGLCCLVPVSLAEDPQGDAAQKTDTSHHDQDAntitrypsin HPTFNKITPNLAEFAFSLYRQLAHQSNSTNIFFSPVSIATAFAMLSLGTKADTHDEILEGLNFNLTEIPEAQIHEGFQELLRTLNQPDSQLQLTTGNGLFLSEGLKLVDKFLEDVKKLYHSEAFTVNFGDTEEAKKQINDYVEKGTQGKIVDLVKELDRDTVFALVNYIFFKGKWERPFEVKDTEEEDFHVDQVTTVKVPMMKRLGMFNIQHCKKLSSWVLLMKYLGNATAIFFLPDEGKLQHLENELTHDIITKFLENEDRRSASLHLPKLSITGTYDLKSVLGQLGITKVFSNGADLSGVTEEAPLKLSKAVHKAVLTIDEKGTEAAGAMFLEAIPMSIPPEVKFNKPFVFLMIE QNTKSPLFMGKVVNPTQK(SEQ ID NO: 2) Alpha-1- AACT MERMLPLLALGLLAAGFCPAVLCHPNSPLDEENLTQENQDRGTAntichymotrypsin HVDLGLASANVDFAFSLYKQLVLKAPDKNVIFSPLSISTALAFLSLGAHNTTLTEILKGLKFNLTETSEAEIHQSFQHLLRTLNQSSDELQLSMGNAMFVKEQLSLLDRFTEDAKRLYGSEAFATDFQDSAAAKKLINDYVKNGTRGKITDLIKDLDSQTMMVLVNYIFFKAKWEMPFDPQDTHQSRFYLSKKKWVMVPMMSLHHLTIPYFRDEELSCTVVELKYTGNASALFILPDQDKMEEVEAMLLPETLKRWRDSLEFREIGELYLPKFSISRDYNLNDILLQLGIEEAFTSKADLSGITGARNLAVSQVVHKAVLDVFEEGTEASAATAVKITLLSALVETRTIVRFNRPF LMIIVPTDTQNIFFMSKVTNPKQA(SEQ ID NO: 3) Apolipoprotein APOA1MKAAVLTLAVLFLTGSQARHFWQQDEPPQSPWDRVKDLATVY A-IVDVLKDSGRDYVSQFEGSALGKQLNLKLLDNWDSVTSTFSKLREQLGPVTQEFWDNLEKETEGLRQEMSKDLEEVKAKVQPYLDDFQKKWQEEMELYRQKVEPLRAELQEGARQKLHELQEKLSPLGEEMRDRARAHVDALRTHLAPYSDELRQRLAARLEALKENGGARLAEYHAKATEHLSTLSEKAKPALEDLRQGLLPVLESFKVSFLSALE EYTKKLNTQ (SEQ ID NO: 4)Cathepsin D CATD MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDR DNNRVGFAEAARL(SEQ ID NO: 5) Carcinoembryonic CEAMGPPSASPHRECIPWQGLLLTASLLNFWNPPTTAKLTIESMPLSV antigen-AEGKEVLLLVHNLPQHLFGYSWYKGERVDGNSLIVGYVIGTQQ related cellATPGAAYSGRETIYTNASLLIQNVTQNDIGFYTLQVIKSDLVNEE adhesionATGQFHVYQENAPGLPVGAVAGIVTGVLVGVALVAALVCFLLL molecule 3AKTGRTSIQRDLKEQQPQALAPGRGPSHSSAFSMSPLSTAQAPLPNPRTAASIYEELLKHDTNIYCRMDHKAEVAS (SEQ ID NO: 6) Clusterin CLUSMMKTLLLFVGLLLTWESGQVLGDQTVSDNELQEMSNQGSKYVNKEIQNAVNGVKQIKTLIEKTNEERKTLLSNLEEAKKKKEDALNETRESETKLKELPGVCNETMMALWEECKPCLKQTCMKFYARVCRSGSGLVGRQLEEFLNQSSPFYFWMNGDRIDSLLENDRQQTHMLDVMQDHFSRASSIIDELFQDRFFTREPQDTYHYLPFSLPHRRPHFFFPKSRIVRSLMPFSPYEPLNFHAMFQPFLEMIHEAQQAMDIHFHSPAFQHPPTEFIREGDDDRTVCREIRHNSTGCLRMKDQCDKCREILSVDCSTNNPSQAKLRRELDESLQVAERLTRKYNELLKSYQWKMLNTSSLLEQLNEQFNWVSRLANLTQGEDQYYLRVTTVASHTSDSDVPSGVTEVVVKLFDSDPITVTVPVEVSRKNPKFMETVAEKALQ EYRKKHREE (SEQ ID NO: 7)Complement CO3 MGPTSGPSLLLLLLTHLPLALGSPMYSIITPNILRLESEETMVLEA C3HDAQGDVPVTVTVHDFPGKKLVLSSEKTVLTPATNHMGNVTFTIPANREFKSEKGRNKFVTVQATFGTQVVEKVVLVSLQSGYLFIQTDKTIYTPGSTVLYRIFTVNHKLLPVGRTVMVNIENPEGIPVKQDSLSSQNQLGVLPLSWDIPELVNMGQWKIRAYYENSPQQVFSTEFEVKEYVLPSFEVIVEPTEKFYYIYNEKGLEVTITARFLYGKKVEGTAFVIFGIQDGEQRISLPESLKRIPIEDGSGEVVLSRKVLLDGVQNPRAEDLVGKSLYVSATVILHSGSDMVQAERSGIPIVTSPYQIHFTKTPKYFKPGMPFDLMVFVTNPDGSPAYRVPVAVQGEDTVQSLTQGDGVAKLSINTHPSQKPLSITVRTKKQELSEAEQATRTMQALPYSTVGNSNNYLHLSVLRTELRPGETLNVNFLLRMDRAHEAKIRYYTYLIMNKGRLLKAGRQVREPGQDLVVLPLSITTDFIPSFRLVAYYTLIGASGQREVVADSVWVDVKDSCVGSLVVKSGQSEDRQPVPGQQMTLKIEGDHGARVVLVAVDKGVFVLNKKNKLTQSKIWDVVEKADIGCTPGSGKDYAGVFSDAGLTFTSSSGQQTAQRAELQCPQPAARRRRSVQLTEKRMDKVGKYPKELRKCCEDGMRENPMRFSCQRRTRFISLGEACKKVFLDCCNYITELRRQHARASHLGLARSNLDEDIIAEENIVSRSEFPESWLWNVEDLKEPPKNGISTKLMNIFLKDSITTWEILAVSMSDKKGICVADPFEVTVMQDFFIDLRLPYSVVRNEQVEIRAVLYNYRQNQELKVRVELLHNPAFCSLATTKRRHQQTVTIPPKSSLSVPYVIVPLKTGLQEVEVKAAVYHHFISDGVRKSLKVVPEGIRMNKTVAVRTLDPERLGREGVQKEDIPPADLSDQVPDTESETRILLQGTPVAQMTEDAVDAERLKHLIVTPSGCGEQNMIGMTPTVIAVHYLDETEQWEKFGLEKRQGALELIKKGYTQQLAFRQPSSAFAAFVKRAPSTWLTAYVVKVFSLAVNLIAIDSQVLCGAVKWLILEKQKPDGVFQEDAPVIHQEMIGGLRNNNEKDMALTAFVLISLQEAKDICEEQVNSLPGSITKAGDFLEANYMNLQRSYTVAIAGYALAQMGRLKGPLLNKFLTTAKDKNRWEDPGKQLYNVEATSYALLALLQLKDFDFVPPVVRWLNEQRYYGGGYGSTQATFMVFQALAQYQKDAPDHQELNLDVSLQLPSRSSKITHRIHWESASLLRSEETKENEGFTVTAEGKGQGTLSVVTMYHAKAKDQLTCNKFDLKVTIKPAPETEKRPQDAKNTMILEICTRYRGDQDATMSILDISMMTGFAPDTDDLKQLANGVDRYISKYELDKAFSDRNTLIIYLDKVSHSEDDCLAFKVHQYFNVELIQPGAVKVYAYYNLEESCTRFYHPEKEDGKLNKLCRDELCRCAEENCFIQKSDDKVTLEERLDKACEPGVDYVYKTRLVKVQLSNDFDEYIMAIEQTIKSGSDEVQVGQQRTFISPIKCREALKLEEKKHYLMWGLSSDFWGEKPNLSYIIGKDTWVEHWPEEDECQDEENQKQCQDLGAFTESMVVFGCPN (SEQ ID NO: 8) Complement CO9MSACRSFAVAICILEISILTAQYTTSYDPELTESSGSASHIDCRMSP C9WSEWSQCDPCLRQMFRSRSIEVFGQFNGKRCTDAVGDRRQCVPTEPCEDAEDDCGNDFQCSTGRCIKMRLRCNGDNDCGDFSDEDDCESEPRPPCRDRVVEESELARTAGYGINILGMDPLSTPFDNEFYNGLCNRDRDGNTLTYYRRPWNVASLIYETKGEKNFRTEHYEEQIEAFKSIIQEKTSNFNAAISLKFTPTETNKAEQCCEETASSISLHGKGSFRFSYSKNETYQLFLSYSSKKEKMFLHVKGEIHLGRFVMRNRDVVLTTTFVDDIKALPTTYEKGEYFAFLETYGTHYSSSGSLGGLYELIYVLDKASMKRKGVELKDIKRCLGYHLDVSLAFSEISVGAEFNKDDCVKRGEGRAVNITSENLIDDVVSLIRGGTRKYAFELKEKLLRGTVIDVTDFVNWASSINDAPVLISQKLSPIYNLVPVKMKNAHLKKQNLERAIEDYINEFSVRKCHTCQNGGTVILMDGKCLCACPFKF EGIACEISKQKISEGLPALEFPNEK(SEQ ID NO: 9) C-reactive CRPMEKLLCFLVLTSLSHAFGQTDMSRKAFVFPKESDTSYVSLKAPL proteinTKPLKAFTVCLHFYTELSSTRGYSIFSYATKRQDNEILIFWSKDIGYSFTVGGSEILFEVPEVTVAPVHICTSWESASGIVEFWVDGKPRVRKSLKKGYTVGAEASIILGQEQDSFGGNFEGSQSLVGDIGNVNMWDFVLSPDEINTIYLGGPFSPNVLNWRALKYEVQGEVFTKPQLW P (SEQ ID NO: 10)Fibrinogen FGB MKRMVSWSFHKLKTMKHLLLLLLCVFLVKSQGVNDNEEGFFSA beta chainRGHRPLDKKREEAPSLRPAPPPISGGGYRARPAKAAATQKKVERKAPDAGGCLHADPDLGVLCPTGCQLQEALLQQERPIRNSVDELNNNVEAVSQTSSSSFQYMYLLKDLWQKRQKQVKDNENVVNEYSSELEKHQLYIDETVNSNIPTNLRVLRSILENLRSKIQKLESDVSAQMEYCRTPCTVSCNIPVVSGKECEEIIRKGGETSEMYLIQPDSSVKPYRVYCDMNTENGGWTVIQNRQDGSVDFGRKWDPYKQGFGNVATNTDGKNYCGLPGEYWLGNDKISQLTRMGPTELLIEMEDWKGDKVKAHYGGFTVQNEANKYQISVNKYRGTAGNALMDGASQLMGENRTMTIHNGMFFSTYDRDNDGWLTSDPRKQCSKEDGGGWWYNRCHAANPNGRYYWGGQYTWDMAKHGTDDGVVWMNWKG SWYSMRKMSMKIRPFFPQQ(SEQ ID NO: 11) Fibrinogen FIBGMSWSLHPRNLILYFYALLFLSSTCVAYVATRDNCCILDERFGSYC gamma chainPTTCGIADFLSTYQTKVDKDLQSLEDILHQVENKTSEVKQLIKAIQLTYNPDESSKPNMIDAATLKSRKMLEEIMKYEASILTHDSSIRYLQEIYNSNNQKIVNLKEKVAQLEAQCQEPCKDTVQIHDITGKDCQDIANKGAKQSGLYFIKPLKANQQFLVYCEIDGSGNGWTVFQKRLDGSVDFKKNWIQYKEGFGHLSPTGTTEFWLGNEKIHLISTQSAIPYALRVELEDWNGRTSTADYAMFKVGPEADKYRLTYAYFAGGDAGDAFDGFDFGDDPSDKFFTSHNGMQFSTWDNDNDKFEGNCAEQDGSGWWMNKCHAGHLNGVYYQGGTYSKASTPNGYDNGIIWATWKTRWYSMKKTTMKIIPFNRLTIGEGQQHHLGGAKQVRPE HPAETEYDSLYPEDDL(SEQ ID NO: 12) Glycine-tRNA GARSMPSPRPVLLRGARAALLLLLPPRLLARPSLLLRRSLSAASCPPISL ligasePAAASRSSMDGAGAEEVLAPLRLAVRQQGDLVRKLKEDKAPQVDVDKAVAELKARKRVLEAKELALQPKDDIVDRAKMEDTLKRRFFYDQAFAIYGGVSGLYDFGPVGCALKNNIIQTWRQHFIQEEQILEIDCTMLTPEPVLKTSGHVDKFADFMVKDVKNGECFRADHLLKAHLQKLMSDKKCSVEKKSEMESVLAQLDNYGQQELADLFVNYNVKSPITGNDLSPPVSFNLMFKTFIGPGGNMPGYLRPETAQGIFLNFKRLLEFNQGKLPFAAAQIGNSFRNEISPRSGLIRVREFTMAEIEHFVDPSEKDHPKFQNVADLHLYLYSAKAQVSGQSARKMRLGDAVEQGVINNTVLGYFIGRIYLYLTKVGISPDKLRFRQHMENEMAHYACDCWDAESKTSYGWIEIVGCADRSCYDLSCHARATKVPLVAEKPLKEPKTVNVVQFEPSKGAIGKAYKKDAKLVMEYLAICDECYITEMEMLLNEKGEFTIETEGKTFQLTKDMINVKRFQKTLYVEEVVPNVIEPSFGLGRIMYTVFEHTFHVREGDEQRTFFSFPAVVAPFKCSVLPLSQNQEFMPFVKELSEALTRHGVSHKVDDSSGSIGRRYARTDEIGVAFGVTIDFDTVNKTPHTATLRDRDSMRQIRAEISELPSIVQDLANGNITWADVEARYPLFEGQETGKKETIEE (SEQ ID NO: 13) Gelsolin GELSMAPHRPAPALLCALSLALCALSLPVRAATASRGASQAGAPQGRVPEARPNSMVVEHPEFLKAGKEPGLQIWRVEKFDLVPVPTNLYGDFFTGDAYVILKTVQLRNGNLQYDLHYWLGNECSQDESGAAAIFTVQLDDYLNGRAVQHREVQGFESATFLGYFKSGLKYKKGGVASGFKHVVPNEVVVQRLFQVKGRRVVRATEVPVSWESFNNGDCFILDLGNNIHQWCGSNSNRYERLKATQVSKGIRDNERSGRARVHVSEEGTEPEAMLQVLGPKPALPAGTEDTAKEDAANRKLAKLYKVSNGAGTMSVSLVADENPFAQGALKSEDCFILDHGKDGKIFVWKGKQANTEERKAALKTASDFITKMDYPKQTQVSVLPEGGETPLFKQFFKNWRDPDQTDGLGLSYLSSHIANVERVPFDAATLHTSTAMAAQHGMDDDGTGQKQIWRIEGSNKVPVDPATYGQFYGGDSYIILYNYRHGGRQGQIIYNWQGAQSTQDEVAASAILTAQLDEELGGTPVQSRVVQGKEPAHLMSLFGGKPMIIYKGGTSREGGQTAPASTRLFQVRANSAGATRAVEVLPKAGALNSNDAFVLKTPSAAYLWVGTGASEAEKTGAQELLRVLRAQPVQVAEGSEPDGFWEALGGKAAYRTSPRLKDKKMDAHPPRLFACSNKIGRFVIEEVPGELMQEDLATDDVMLLDTWDQVFVWVGKDSQEEEKTEALTSAKRYIETDPANRDRRTPITVVKQGFEPPSFVGWFLGWDDDYWSVDPLDRAMAEL AA (SEQ ID NO: 14)Haptoglobin HPT MSALGAVIALLLWGQLFAVDSGNDVTDIADDGCPKPPEIAHGYVEHSVRYQCKNYYKLRTEGDGVYTLNDKKQWINKAVGDKLPECEADDGCPKPPEIAHGYVEHSVRYQCKNYYKLRTEGDGVYTLNNEKQWINKAVGDKLPECEAVCGKPKNPANPVQRILGGHLDAKGSFPWQAKMVSHHNLTTGATLINEQWLLTTAKNLFLNHSENATAKDIAPTLTLYVGKKQLVEIEKVVLHPNYSQVDIGLIKLKQKVSVNERVMPICLPSKDYAEVGRVGYVSGWGRNANFKFTDHLKYVMLPVADQDQCIRHYEGSTVPEKKTPKSPVGVQPILNEHTFCAGMSKYQEDTCYGDAGSAFAVHDLEEDTWYATGILSFDKSCAVAEYGVY VKVTSIQDWVQKTIAEN(SEQ ID NO: 15) Macrophage MIFMPMFIVNTNVPRASVPDGFLSELTQQLAQATGKPPQYIAVHVVP migrationDQLMAFGGSSEPCALCSLHSIGKIGGAQNRSYSKLLCGLLAERLR inhibitoryISPDRVYINYYDMNAANVGWNNSTFA factor (SEQ ID NO: 16) Osteopontin OSTPMRIAVICFCLLGITCAIPVKQADSGSSEEKQLYNKYPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSKSNESHDHMDDMDDEDDDDHVDSQDSIDSNDSDDVDDTDDSHQSDESHHSDESDELVTDFPTDLPATEVFTPVVPTVDTYDGRGDSVVYGLRSKSKKFRRPDIQYPDATDEDITSHMESEELNGAYKAIPVAQDLNAPSDWDSRGKDSYETSQLDDQSAETHSHKQSRLYKRKANDESNEHSDVIDSQELSKVSREFHSHEFHSHEDMLVVDPKSKEEDKHLKFRISHELD SASSEVN (SEQ ID NO: 17)Peroxiredoxin-1 PRDX1 MSSGNAKIGHPAPNFKATAVMPDGQFKDISLSDYKGKYVVFFFYPLDFTFVCPTEIIAFSDRAEEFKKLNCQVIGASVDSHFCHLAWVNTPKKQGGLGPMNIPLVSDPKRTIAQDYGVLKADEGISFRGLFIIDDKGILRQITVNDLPVGRSVDETLRLVQAFQFTDKHGEVCPAGWK PGSDTIKPDVQKSKEYFSKQK(SEQ ID NO: 18) P-Selectin PSGLMPLQLLLLLILLGPGNSLQLWDTWADEAEKALGPLLARDRRQA glycoproteinTEYEYLDYDFLPETEPPEMLRNSTDTTPLTGPGTPESTTVEPAAR ligand 1RSTGLDAGGAVTELTTELANMGNLSTDSAAMEIQTTQPAATEAQTTQPVPTEAQTTPLAATEAQTTRLTATEAQTTPLAATEAQTTPPAATEAQTTQPTGLEAQTTAPAAMEAQTTAPAAMEAQTTPPAAMEAQTTQTTAMEAQTTAPEATEAQTTQPTATEAQTTPLAAMEALSTEPSATEALSMEPTTKRGLFIPFSVSSVTHKGIPMAASNLSVNYPVGAPDHISVKQCLLAILILALVATIFFVCTVVLAVRLSRKGHMYPVRNYSPTEMVCISSLLPDGGEGPSATANGGLSKAKSPGLTPEPRED REGDDLTLHSFLP(SEQ ID NO: 19) S100A8 S10A8MLTELEKALNSIIDVYHKYSLIKGNFHAVYRDDLKKLLETECPQYIRKKGADVWFKELDINTDGAVNFQEFLILVIKMGVAAHKKSHE ESHKE (SEQ ID NO: 20)S100A9 S10A9 MTCKMSQLERNIETIINTFHQYSVKLGHPDTLNQGEFKELVRKDLQNFLKKENKNEKVIEHIMEDLDTNADKQLSFEEFIMLMARLTW ASHEKMHEGDEGPGHHHKPGLGEGTP(SEQ ID NO: 21) Serum SAA1 MKLLTGLVFCSLVLGVSSRSFFSFLGEAFDGARDMWRAYSDMRamyloid A-1 EANYIGSDKYFHARGNYDAAKRGPGGVWAAEAISDARENIQRF proteinFGHGAEDSLADQAANEWGRSGKDPNHFRPAGLPEKY (SEQ ID NO: 22) Selenium- SBP1MATKCGNCGPGYSTPLEAMKGPREEIVYLPCIYRNTGTEAPDYL bindingATVDVDPKSPQYCQVIHRLPMPNLKDELHHSGWNTCSSCFGDST protein 1KSRTKLVLPSLISSRIYVVDVGSEPRAPKLHKVIEPKDIHAKCELAFLHTSHCLASGEVMISSLGDVKGNGKGGFVLLDGETFEVKGTWERPGGAAPLGYDFWYQPRHNVMISTEWAAPNVLRDGFNPADVEAGLYGSHLYVWDWQRHEIVQTLSLKDGLIPLEIRFLHNPDAAQGFVGCALSSTIQRFYKNEGGTWSVEKVIQVPPKKVKGWLLPEMPGLITDILLSLDDRFLYFSNWLHGDLRQYDISDPQRPRLTGQLFLGGSIVKGGPVQVLEDEELKSQPEPLVVKGKRVAGGPQMIQLSLDGKRLYITTSLYSAWDKQFYPDLIREGSVMLQVDVDTVKGGLKLNPNFLVDFGKEPLGPALAHELRYPGGDCSSDIWI (SEQ ID NO: 23) Seprase SEPRMKTWVKIVFGVATSAVLALLVMCIVLRPSRVHNSEENTMRALTLKDILNGTFSYKTFFPNWISGQEYLHQSADNNIVLYNIETGQSYTILSNRTMKSVNASNYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLSNGEFVRGNELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITFNGRENKIFNGIPDWVYEEEMLATKYALWWSPNGKFLAYAEFNDTDIPVIAYSYYGDEQYPRTINIPYPKAGAKNPVVRIFIIDTTYPAYVGPQEVPVPAMIASSDYYFSWLTWVTDERVCLQWLKRVQNVSVLSICDFREDWQTWDCPKTQEHIEESRTGWAGGFFVSTPVFSYDAISYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAINIFRVTQDSLFYSSNEFEEYPGRRNIYRISIGSYPPSKKCVTCHLRKERCQYYTASFSDYAKYYALVCYGPGIPISTLHDGRTDQEIKILEENKELENALKNIQLPKEEIKKLEVDEITLWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVRSVFAVNWISYLASKEGMVIALVDGRGTAFQGDKLLYAVYRKLGVYEVEDQITAVRKFIEMGFIDEKRIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASVYTERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGLSGLSTNHLYTHMTHFLK QCFSLSD (SEQ ID NO: 24)Transferrin TFRC MMDQARSAFSNLFGGEPLSYTRFSLARQVDGDNSHVEMKLAVD ReceptorEEENADNNTKANVTKPKRCSGSICYGTIAVIVFFLIGFMIGYLGY Protein 1CKGVEPKTECERLAGTESPVREEPGEDFPAARRLYWDDLKRKLSEKLDSTDFTGTIKLLNENSYVPREAGSQKDENLALYVENQFREFKLSKVWRDQHFVKIQVKDSAQNSVIIVDKNGRLVYLVENPGGYVAYSKAATVTGKLVHANFGTKKDFEDLYTPVNGSIVIVRAGKITFAEKVANAESLNAIGVLIYMDQTKFPIVNAELSFFGHAHLGTGDPYTPGFPSFNHTQFPPSRSSGLPNIPVQTISRAAAEKLFGNMEGDCPSDWKTDSTCRMVTSESKNVKLTVSNVLKEIKILNIFGVIKGFVEPDHYVVVGAQRDAWGPGAAKSGVGTALLLKLAQMFSDMVLKDGFQPSRSIIFASWSAGDFGSVGATEWLEGYLSSLHLKAFTYINLDKAVLGTSNFKVSASPLLYTLIEKTMQNVKHPVTGQFLYQDSNWASKVEKLTLDNAAFPFLAYSGIPAVSFCFCEDTDYPYLGTTMDTYKELIERIPELNKVARAAAEVAGQFVIKLTHDVELNLDYERYNSQLLSFVRDLNQYRADIKEMGLSLQWLYSARGDFFRATSRLTTDFGNAEKTDRFVMKKLNDRVMRVEYHFLSPYVSPKESPFRHVFWGSGSHTLPALLENLKLRKQNNGAFNETLFRNQLALATWTIQGAAN ALSGDVWDIDNEF(SEQ ID NO: 25) Growth / GDF15MPGQELRTVNGSQMLLVLLVLSWLPHGGALSLAEASRASFPGPS differentiationELHSEDSRFRELRKRYEDLLTRLRANQSWEDSNTDLVPAPAVRI factor 15LTPEVRLGSGGHLHLRISRAALPEGLPEASRLHRALFRLSPTASRSWDVTRPLRRQLSLARPQAPALHLRLSPPPSQSDQLLAESSSARPQLELHLRPQAARGRRRARARNGDHCPLGPGRCCRLHTVRASLEDLGWADWVLSPREVQVTMCIGACPSQFRAANMHAQIKTSLHRLKPDTVPAPCCVPASYNPMVLIQKTDTGVSLQTYDDLLAKDCHCI (SEQ ID NO: 26)

Biomarkers contemplated herein also include polypeptides having an aminoacid sequence identical to a listed marker of Table 1 over a span of 8residues, 9, residues, 10 residues, 20 residues, 50 residues, oralternately 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70% 80% 90%, 95% or graterthan 95% of the sequence of the biomarker. Variant or alternative formsof the biomarker include for example polypeptides encoded by anysplice-variants of transcripts encoding the disclosed biomarkers. Incertain cases the modified forms, fragments, or their corresponding RNAor DNA, may exhibit better discriminatory power in diagnosis than thefull-length protein.

Biomarkers contemplated herein also include truncated forms orpolypeptide fragments of any of the proteins described herein. Truncatedforms or polypeptide fragments of a protein can include N-terminallydeleted or truncated forms and C-terminally deleted or truncated forms.Truncated forms or fragments of a protein can include fragments arisingby any mechanism, such as, without limitation, by alternativetranslation, exo- and/or endo-proteolysis and/or degradation, forexample, by physical, chemical and/or enzymatic proteolysis. Withoutlimitation, a biomarker may comprise a truncated or fragment of aprotein, polypeptide or peptide may represent about 1%, 2%, 3%, 4%, 5%,6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%,91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% of the amino acidsequence of the protein.

Without limitation, a truncated or fragment of a protein may include asequence of about 5-20 consecutive amino acids, or about 10-50consecutive amino acids, or about 20-100 consecutive amino acids, orabout 30-150 consecutive amino acids, or about 50-500 consecutive aminoacid residues of the corresponding full length protein.

In some instances, a fragment is N-terminally and/or C-terminallytruncated by between 1 and about 20 amino acids, such as, for example,by between 1 and about 15 amino acids, or by between 1 and about 10amino acids, or by between 1 and about 5 amino acids, compared to thecorresponding mature, full-length protein or its soluble or plasmacirculating form.

Any protein biomarker of the present disclosure such as a peptide,polypeptide or protein and fragments thereof may also encompass modifiedforms of said marker, peptide, polypeptide or protein and fragments suchas bearing post-expression modifications including but not limited to,modifications such as phosphorylation, glycosylation, lipidation,methylation, selenocystine modification, cysteinylation, sulphonation,glutathionylation, acetylation, oxidation of methionine to methioninesulphoxide or methionine sulphone, and the like.

In some instances, a fragmented protein is N-terminally and/orC-terminally truncated. Such fragmented protein can comprise one ormore, or all transitional ions of the N-terminally (a, b, c-ion) and/orC-terminally (x, y, z-ion) truncated protein or peptide. Exemplary humanmarkers, nucleic acids, proteins or polypeptides as taught herein are asannotated under NCBI Genbank (accessible at the websitencbi.nlm.nih.gov) or Swissprot/Uniprot (accessible at the websiteuniprot.org) accession numbers. In some instances said sequences are ofprecursors (for example, preproteins) of the of markers, nucleic acids,proteins or polypeptides as taught herein and may include parts whichare processed away from mature molecules. In some instances althoughonly one or more isoforms is disclosed, all isoforms of the sequencesare intended.

Antibodies for the detection of the biomarkers listed herein arecommercially available. A partial list of sources for reagents usefulfor the assay of biomarkers herein is presented in Table 2 below.

TABLE 2 Reagent Sources ELISA Assay Reference Plasma Abbrev. Kit VendorReference Vendor Dilution A1AT Genway Biotech, San Diego, CA Nativeprotein MyBiosource, San Diego, CA 1:240,000 A1AG1 R&D Systems,Minneapolis, MN Native protein BioVendor, Asheville, NC 1:20,000 AACTGenway Biotech, San Diego, CA Native protein MyBiosource, San Diego, CA1:10,000 ANXA1 Cloud Clone, Wuhan, PRC Recombinant protein Origene,Rockville, MD 1:8,000 APOA1 Cusabio, Wuhan, PRC Native proteinMyBiosource, San Diego, CA 1:800 CRP BioVendor, Asheville, NCRecombinant protein R&D Systems, Minneapolis, MN 1:1,000 CAH1 CloudClone, Wuhan, PRC Recombinant protein MyBiosource, San Diego, CA 1:32CEA IBL International, Toronto, ON Native protein Origene, Rockville, MD1:1 CATD AbCam, Cambridge, MA Native protein Novus Biologicals,Littleton, CA 1:250 CLUS BioVendor, Asheville, NC Native proteinMyBiosource, San Diego, CA 1:3,000 CO3 Abnova, Taipei, Taiwan Nativeprotein MyBiosource, San Diego, CA 1:250 CO9 AssayPro, St. Charles, MONative protein MyBiosource, San Diego, CA 1:20,000 DPP4 Cloud Clone,Wuhan, PRC Native protein BioVendor, Asheville, NC 1:2,000 FGB CloudClone, Wuhan, PRC Recombinant protein Antibodies Online, Atlanta, GA1:8,000 FIBG Cloud Clone, Wuhan, PRC Native protein MyBiosource, SanDiego, CA 1:8,000 GELS Cloud Clone, Wuhan, PRC Recombinant proteinOrigene, Rockville, MD 1:100 GARS Cloud Clone, Wuhan, PRC Recombinantprotein Novus Biologicals, Littleton, CA 1:40 GDF15 R&D Systems,Minneapolis, MN Native protein AbCam, Cambridge, MA 1:8 HPT AssayPro,St. Charles, MO Recombinant protein Origene, Rockville, MD 1:2,000 MIFR&D Systems, Minneapolis, MN Recombinant protein MyBiosource, San Diego,CA 1:10 OSTP R&D Systems, Minneapolis, MN Recombinant protein Origene,Rockville, MD 1:20 PSGL IBL America, Minneapolis, MN Recombinant proteinLife Technologies, Camarillo, CA 1:30 PRDX1 Cloud Clone, Wuhan, PRCRecombinant protein MyBiosource, San Diego, CA 1:100 SBP1 Cloud Clone,Wuhan, PRC Recombinant protein Origene, Rockville, MD 1:16 SEPR R&DSystems, Minneapolis, MN Recombinant protein Origene, Rockville, MD 1:40SAA1 Life Technologies, Camarillo, CA Recombinant protein Origene,Rockville, MD 1:240 TIMP1 R&D Systems, Minneapolis, MN Recombinantprotein Life Technologies, Camarillo, CA 1:100 TFRC Cloud Clone, Wuhan,PRC Native protein MyBiosource, San Diego, CA 1:250 TFF3 R&D Systems,Minneapolis, MN Recombinant protein R&D Systems, Minneapolis, MN 1:50PKM2 ScheBo, Giessen, GER Recombinant protein Origene, Rockville, MD1:100

For a given biomarker panel recited herein, variant biomarker panelsdiffering in one or more than one constituent are also contemplated.Thus, turning to a lead CRC panel AACT, CATD, CEA, CO3, CO9, MIF, PSGL,and SEPR as an example, a number of related panels are disclosed. Forthis and other panels disclosed herein, variants are contemplatedcomprising at least 8, at least 7, at least 6, at least 5, at least 4,at least 3, or at least 2 of the biomarker constituents of a recitedbiomarker panel. Thus, turning to a lead biomarker panel AACT, CATD,CEA, CO3, CO9, MIF, PSGL, and SEPR, one sees the variant panels aslisted in Table 3. Age is optionally included as a non-protein featurefor any of the panel variants listed herein

TABLE 3 CRC Panel Embodiments Protein Nonprotein No. Features Panelfeature 1 8 AACT, CATD, CEA, CO3, +/−Age CO9, MIF, PSGL, SEPR 2 7 AACT,CATD, CEA, CO3, CO9, MIF, PSGL +/−Age 3 7 AACT, CATD, CEA, CO3, CO9,MIF, SEPR +/−Age 4 7 AACT, CATD, CEA, CO3, CO9, PSGL, SEPR +/−Age 5 7AACT, CATD, CEA, CO3, MIF, PSGL, SEPR +/−Age 6 7 AACT, CATD, CEA, CO9,MIF, PSGL, SEPR +/−Age 7 7 AACT, CATD, CO3, CO9, MIF, PSGL, SEPR +/−Age8 7 AACT, CEA, CO3, CO9, MIF, PSGL, SEPR +/−Age 9 7 CATD, CEA, CO3, CO9,MIF, PSGL, SEPR +/−Age 10 6 AACT, CATD, CEA, CO3, CO9, MIF +/−Age 11 6AACT, CATD, CEA, CO3, CO9, PSGL +/−Age 12 6 AACT, CATD, CEA, CO3, CO9,SEPR +/−Age 13 6 AACT, CATD, CEA, CO3, MIF, PSGL +/−Age 14 6 AACT, CATD,CEA, CO3, MIF, SEPR +/−Age 15 6 AACT, CATD, CEA, CO3, PSGL, SEPR +/−Age16 6 AACT, CATD, CEA, CO9, MIF, PSGL +/−Age 17 6 AACT, CATD, CEA, CO9,MIF, SEPR +/−Age 18 6 AACT, CATD, CEA, CO9, PSGL, SEPR +/−Age 19 6 AACT,CATD, CEA, MIF, PSGL, SEPR +/−Age 20 6 AACT, CATD, CO3, CO9, MIF, PSGL+/−Age 21 6 AACT, CATD, CO3, CO9, MIF, SEPR +/−Age 22 6 AACT, CATD, CO3,CO9, PSGL, SEPR +/−Age 23 6 AACT, CATD, CO3, MIF, PSGL, SEPR +/−Age 24 6AACT, CATD, CO9, MIF, PSGL, SEPR +/−Age 25 6 AACT, CEA, CO3, CO9, MIF,PSGL +/−Age 26 6 AACT, CEA, CO3, CO9, MIF, SEPR +/−Age 27 6 AACT, CEA,CO3, CO9, PSGL, SEPR +/−Age 28 6 AACT, CEA, CO3, MIF, PSGL, SEPR +/−Age29 6 AACT, CEA, CO9, MIF, PSGL, SEPR +/−Age 30 6 AACT, CO3, CO9, MIF,PSGL, SEPR +/−Age 31 6 CATD, CEA, CO3, CO9, MIF, PSGL +/−Age 32 6 CATD,CEA, CO3, CO9, MIF, SEPR +/−Age 33 6 CATD, CEA, CO3, CO9, PSGL, SEPR+/−Age 34 6 CATD, CEA, CO3, MIF, PSGL, SEPR +/−Age 35 6 CATD, CEA, CO9,MIF, PSGL, SEPR +/−Age 36 6 CATD, CO3, CO9, MIF, PSGL, SEPR +/−Age 37 6CEA, CO3, CO9, MIF, PSGL, SEPR +/−Age 38 5 AACT, CATD, CEA, CO3, CO9+/−Age 39 5 AACT, CATD, CEA, CO3, MIF +/−Age 40 5 AACT, CATD, CEA, CO3,PSGL +/−Age 41 5 AACT, CATD, CEA, CO3, SEPR +/−Age 42 5 AACT, CATD, CEA,CO9, MIF +/−Age 43 5 AACT, CATD, CEA, CO9, PSGL +/−Age 44 5 AACT, CATD,CEA, CO9, SEPR +/−Age 45 5 AACT, CATD, CEA, MIF, PSGL +/−Age 46 5 AACT,CATD, CEA, MIF, SEPR +/−Age 47 5 AACT, CATD, CEA, PSGL, SEPR +/−Age 48 5AACT, CATD, CO3, CO9, MIF +/−Age 49 5 AACT, CATD, CO3, CO9, PSGL +/−Age50 5 AACT, CATD, CO3, CO9, SEPR +/−Age 51 5 AACT, CATD, CO3, MIF, PSGL+/−Age 52 5 AACT, CATD, CO3, MIF, SEPR +/−Age 53 5 AACT, CATD, CO3,PSGL, SEPR +/−Age 54 5 AACT, CATD, CO9, MIF, PSGL +/−Age 55 5 AACT,CATD, CO9, MIF, SEPR +/−Age 56 5 AACT, CATD, CO9, PSGL, SEPR +/−Age 57 5AACT, CATD, MIF, PSGL, SEPR +/−Age 58 5 AACT, CEA, CO3, CO9, MIF +/−Age59 5 AACT, CEA, CO3, CO9, PSGL +/−Age 60 5 AACT, CEA, CO3, CO9, SEPR+/−Age 61 5 AACT, CEA, CO3, MIF, PSGL +/−Age 62 5 AACT, CEA, CO3, MIF,SEPR +/−Age 63 5 AACT, CEA, CO3, PSGL, SEPR +/−Age 64 5 AACT, CEA, CO9,MIF, PSGL +/−Age 65 5 AACT, CEA, CO9, MIF, SEPR +/−Age 66 5 AACT, CEA,CO9, PSGL, SEPR +/−Age 67 5 AACT, CEA, MIF, PSGL, SEPR +/−Age 68 5 AACT,CO3, CO9, MIF, PSGL +/−Age 69 5 AACT, CO3, CO9, MIF, SEPR +/−Age 70 5AACT, CO3, CO9, PSGL, SEPR +/−Age 71 5 AACT, CO3, MIF, PSGL, SEPR +/−Age72 5 AACT, CO9, MIF, PSGL, SEPR +/−Age 73 5 CATD, CEA, CO3, CO9, MIF+/−Age 74 5 CATD, CEA, CO3, CO9, PSGL +/−Age 75 5 CATD, CEA, CO3, CO9,SEPR +/−Age 76 5 CATD, CEA, CO3, MIF, PSGL +/−Age 77 5 CATD, CEA, CO3,MIF, SEPR +/−Age 78 5 CATD, CEA, CO3, PSGL, SEPR +/−Age 79 5 CATD, CEA,CO9, MIF, PSGL +/−Age 80 5 CATD, CEA, CO9, MIF, SEPR +/−Age 81 5 CATD,CEA, CO9, PSGL, SEPR +/−Age 82 5 CATD, CEA, MIF, PSGL, SEPR +/−Age 83 5CATD, CO3, CO9, MIF, PSGL +/−Age 84 5 CATD, CO3, CO9, MIF, SEPR +/−Age85 5 CATD, CO3, CO9, PSGL, SEPR +/−Age 86 5 CATD, CO3, MIF, PSGL, SEPR+/−Age 87 5 CATD, CO9, MIF, PSGL, SEPR +/−Age 88 5 CEA, CO3, CO9, MIF,PSGL +/−Age 89 5 CEA, CO3, CO9, MIF, SEPR +/−Age 90 5 CEA, CO3, CO9,PSGL, SEPR +/−Age 91 5 CEA, CO3, MIF, PSGL, SEPR +/−Age 92 5 CEA, CO9,MIF, PSGL, SEPR +/−Age 93 5 CO3, CO9, MIF, PSGL, SEPR +/−Age 94 4 AACT,CATD, CEA, CO3 +/−Age 95 4 AACT, CATD, CEA, CO9 +/−Age 96 4 AACT, CATD,CEA, MIF +/−Age 97 4 AACT, CATD, CEA, PSGL +/−Age 98 4 AACT, CATD, CEA,SEPR +/−Age 99 4 AACT, CATD, CO3, CO9 +/−Age 100 4 AACT, CATD, CO3, MIF+/−Age 101 4 AACT, CATD, CO3, PSGL +/−Age 102 4 AACT, CATD, CO3, SEPR+/−Age 103 4 AACT, CATD, CO9, MIF +/−Age 104 4 AACT, CATD, CO9, PSGL+/−Age 105 4 AACT, CATD, CO9, SEPR +/−Age 106 4 AACT, CATD, MIF, PSGL+/−Age 107 4 AACT, CATD, MIF, SEPR +/−Age 108 4 AACT, CATD, PSGL, SEPR+/−Age 109 4 AACT, CEA, CO3, CO9 +/−Age 110 4 AACT, CEA, CO3, MIF +/−Age111 4 AACT, CEA, CO3, PSGL +/−Age 112 4 AACT, CEA, CO3, SEPR +/−Age 1134 AACT, CEA, CO9, MIF +/−Age 114 4 AACT, CEA, CO9, PSGL +/−Age 115 4AACT, CEA, CO9, SEPR +/−Age 116 4 AACT, CEA, MIF, PSGL +/−Age 117 4AACT, CEA, MIF, SEPR +/−Age 118 4 AACT, CEA, PSGL, SEPR +/−Age 119 4AACT, CO3, CO9, MIF +/−Age 120 4 AACT, CO3, CO9, PSGL +/−Age 121 4 AACT,CO3, CO9, SEPR +/−Age 122 4 AACT, CO3, MIF, PSGL +/−Age 123 4 AACT, CO3,MIF, SEPR +/−Age 124 4 AACT, CO3, PSGL, SEPR +/−Age 125 4 AACT, CO9,MIF, PSGL +/−Age 126 4 AACT, CO9, MIF, SEPR +/−Age 127 4 AACT, CO9,PSGL, SEPR +/−Age 128 4 AACT, MIF, PSGL, SEPR +/−Age 129 4 CATD, CEA,CO3, CO9 +/−Age 130 4 CATD, CEA, CO3, MIF +/−Age 131 4 CATD, CEA, CO3,PSGL +/−Age 132 4 CATD, CEA, CO3, SEPR +/−Age 133 4 CATD, CEA, CO9, MIF+/−Age 134 4 CATD, CEA, CO9, PSGL +/−Age 135 4 CATD, CEA, CO9, SEPR+/−Age 136 4 CATD, CEA, MIF, PSGL +/−Age 137 4 CATD, CEA, MIF, SEPR+/−Age 138 4 CATD, CEA, PSGL, SEPR +/−Age 139 4 CATD, CO3, CO9, MIF+/−Age 140 4 CATD, CO3, CO9, PSGL +/−Age 141 4 CATD, CO3, CO9, SEPR+/−Age 142 4 CATD, CO3, MIF, PSGL +/−Age 143 4 CATD, CO3, MIF, SEPR+/−Age 144 4 CATD, CO3, PSGL, SEPR +/−Age 145 4 CATD, CO9, MIF, PSGL+/−Age 146 4 CATD, CO9, MIF, SEPR +/−Age 147 4 CATD, CO9, PSGL, SEPR+/−Age 148 4 CATD, MIF, PSGL, SEPR +/−Age 149 4 CEA, CO3, CO9, MIF+/−Age 150 4 CEA, CO3, CO9, PSGL +/−Age 151 4 CEA, CO3, CO9, SEPR +/−Age152 4 CEA, CO3, MIF, PSGL +/−Age 153 4 CEA, CO3, MIF, SEPR +/−Age 154 4CEA, CO3, PSGL, SEPR +/−Age 155 4 CEA, CO9, MIF, PSGL +/−Age 156 4 CEA,CO9, MIF, SEPR +/−Age 157 4 CEA, CO9, PSGL, SEPR +/−Age 158 4 CEA, MIF,PSGL, SEPR +/−Age 159 4 CO3, CO9, MIF, PSGL +/−Age 160 4 CO3, CO9, MIF,SEPR +/−Age 161 4 CO3, CO9, PSGL, SEPR +/−Age 162 4 CO3, MIF, PSGL, SEPR+/−Age 163 4 CO9, MIF, PSGL, SEPR +/−Age 164 3 AACT, CATD, CEA +/−Age165 3 AACT, CATD, CO3 +/−Age 166 3 AACT, CATD, CO9 +/−Age 167 3 AACT,CATD, MIF +/−Age 168 3 AACT, CATD, PSGL +/−Age 169 3 AACT, CATD, SEPR+/−Age 170 3 AACT, CEA, CO3 +/−Age 171 3 AACT, CEA, CO9 +/−Age 172 3AACT, CEA, MIF +/−Age 173 3 AACT, CEA, PSGL +/−Age 174 3 AACT, CEA, SEPR+/−Age 175 3 AACT, CO3, CO9 +/−Age 176 3 AACT, CO3, MIF +/−Age 177 3AACT, CO3, PSGL +/−Age 178 3 AACT, CO3, SEPR +/−Age 179 3 AACT, CO9, MIF+/−Age 180 3 AACT, CO9, PSGL +/−Age 181 3 AACT, CO9, SEPR +/−Age 182 3AACT, MIF, PSGL +/−Age 183 3 AACT, MIF, SEPR +/−Age 184 3 AACT, PSGL,SEPR +/−Age 185 3 CATD, CEA, CO3 +/−Age 186 3 CATD, CEA, CO9 +/−Age 1873 CATD, CEA, MIF +/−Age 188 3 CATD, CEA, PSGL +/−Age 189 3 CATD, CEA,SEPR +/−Age 190 3 CATD, CO3, CO9 +/−Age 191 3 CATD, CO3, MIF +/−Age 1923 CATD, CO3, PSGL +/−Age 193 3 CATD, CO3, SEPR +/−Age 194 3 CATD, CO9,MIF +/−Age 195 3 CATD, CO9, PSGL +/−Age 196 3 CATD, CO9, SEPR +/−Age 1973 CATD, MIF, PSGL +/−Age 198 3 CATD, MIF, SEPR +/−Age 199 3 CATD, PSGL,SEPR +/−Age 200 3 CEA, CO3, CO9 +/−Age 201 3 CEA, CO3, MIF +/−Age 202 3CEA, CO3, PSGL +/−Age 203 3 CEA, CO3, SEPR +/−Age 204 3 CEA, CO9, MIF+/−Age 205 3 CEA, CO9, PSGL +/−Age 206 3 CEA, CO9, SEPR +/−Age 207 3CEA, MIF, PSGL +/−Age 208 3 CEA, MIF, SEPR +/−Age 209 3 CEA, PSGL, SEPR+/−Age 210 3 CO3, CO9, MIF +/−Age 211 3 CO3, CO9, PSGL +/−Age 212 3 CO3,CO9, SEPR +/−Age 213 3 CO3, MIF, PSGL +/−Age 214 3 CO3, MIF, SEPR +/−Age215 3 CO3, PSGL, SEPR +/−Age 216 3 CO9, MIF, PSGL +/−Age 217 3 CO9, MIF,SEPR +/−Age 218 3 CO9, PSGL, SEPR +/−Age 219 3 MIF, PSGL, SEPR +/−Age220 2 AACT, CATD +/−Age 221 2 AACT, CEA +/−Age 222 2 AACT, CO3 +/−Age223 2 AACT, CO9 +/−Age 224 2 AACT, MIF +/−Age 225 2 AACT, PSGL +/−Age226 2 AACT, SEPR +/−Age 227 2 CATD, CEA +/−Age 228 2 CATD, CO3 +/−Age229 2 CATD, CO9 +/−Age 230 2 CATD, MIF +/−Age 231 2 CATD, PSGL +/−Age232 2 CATD, SEPR +/−Age 233 2 CEA, CO3 +/−Age 234 2 CEA, CO9 +/−Age 2352 CEA, MIF +/−Age 236 2 CEA, PSGL +/−Age 237 2 CEA, SEPR +/−Age 238 2CO3, CO9 +/−Age 239 2 CO3, MIF +/−Age 240 2 CO3, PSGL +/−Age 241 2 CO3,SEPR +/−Age 242 2 CO9, MIF +/−Age 243 2 CO9, PSGL +/−Age 244 2 CO9, SEPR+/−Age 245 2 MIF, PSGL +/−Age 246 2 MIF, SEPR +/−Age 247 2 PSGL, SEPR+/−Age

In some embodiments a biomarker comprises 8 or more proteins, wherein 8or more of the proteins comprise: AACT, CATD, CEA, CO3, CO9, MIF, PSGL,and SEPR. In some embodiments a biomarker panel comprises 7 or moreproteins, wherein 7 of the proteins comprises AACT, CATD, CEA, CO3, CO9,MIF, and PSGL. In some embodiments a biomarker panel comprises 7 or moreproteins, wherein 7 of the proteins comprises AACT, CATD, CEA, CO3, CO9,MIF, and SEPR. In some embodiments a biomarker panel comprises 7 or moreproteins, wherein 7 of the proteins comprises AACT, CATD, CEA, CO3, CO9,PSGL, and SEPR. In some embodiments a biomarker panel comprises 7 ormore proteins, wherein 7 of the proteins comprises AACT, CATD, CEA, CO3,MIF, PSGL, and SEPR. In some embodiments a biomarker panel comprises 7or more proteins, wherein 7 of the proteins comprises AACT, CATD, CEA,CO9, MIF, PSGL, and SEPR. In some embodiments a biomarker panelcomprises 7 or more proteins, wherein 7 of the proteins comprises AACT,CATD, CO3, CO9, MIF, PSGL, and SEPR. In some embodiments a biomarkerpanel comprises 7 or more proteins, wherein 7 of the proteins comprisesAACT, CEA, CO3, CO9, MIF, PSGL, and SEPR. In some embodiments abiomarker panel comprises 7 or more proteins, wherein 7 of the proteinscomprises CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR.

In some embodiments a biomarker panel comprises 6 or more proteins,wherein 6 of the proteins comprises AACT, CATD, CEA, CO3, CO9, and MIF.In some embodiments a biomarker panel comprises 6 or more proteins,wherein 6 of the proteins comprises AACT, CATD, CEA, CO3, CO9, and PSGL.In some embodiments a biomarker panel comprises 6 or more proteins,wherein 6 of the proteins comprises AACT, CATD, CEA, CO3, CO9, and SEPR.In some embodiments a biomarker panel comprises 6 or more proteins,wherein 6 of the proteins comprises AACT, CATD, CEA, CO3, MIF, and PSGL.In some embodiments a biomarker panel comprises 6 or more proteins,wherein 6 of the proteins comprises AACT, CATD, CEA, CO3, MIF, and SEPR.In some embodiments a biomarker panel comprises 6 or more proteins,wherein 6 of the proteins comprises AACT, CATD, CEA, CO3, PSGL, andSEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CATD, CEA, CO9, MIF,and PSGL. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CATD, CEA, CO9, MIF,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CATD, CEA, CO9,PSGL, and SEPR. In some embodiments a biomarker panel comprises 6 ormore proteins, wherein 6 of the proteins comprises AACT, CATD, CEA, MIF,PSGL, and SEPR. In some embodiments a biomarker panel comprises 6 ormore proteins, wherein 6 of the proteins comprises AACT, CATD, CO3, CO9,MIF, and PSGL. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CATD, CO3, CO9, MIF,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CATD, CO3, CO9,PSGL, and SEPR. In some embodiments a biomarker panel comprises 6 ormore proteins, wherein 6 of the proteins comprises AACT, CATD, CO3, MIF,PSGL, and SEPR. In some embodiments a biomarker panel comprises 6 ormore proteins, wherein 6 of the proteins comprises AACT, CATD, CO9, MIF,and PSGL, and SEPR. In some embodiments a biomarker panel comprises 6 ormore proteins, wherein 6 of the proteins comprises AACT, CEA, CO3, CO9,MIF, and PSGL. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CEA, CO3, CO9, MIF,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CEA, CO3, CO9, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CEA, CO3, MIF, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CEA, CO9, MIF, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises AACT, CO3, CO9, MIF, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises CATD, CEA, CO3, CO9, MIF,and PSGL. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises CATD, CEA, CO3, CO9, MIF,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises CATD, CEA, CO3, CO9, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises CATD, CEA, CO3, MIF, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises CATD, CEA, CO9, MIF, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises CATD, CO3, CO9, MIF, PSGL,and SEPR. In some embodiments a biomarker panel comprises 6 or moreproteins, wherein 6 of the proteins comprises CEA, CO3, CO9, MIF, PSGL,and SEPR.

In some embodiments a biomarker panel comprises 5 or more proteins,wherein 5 of the proteins comprises AACT, CATD, CEA, CO3, and CO9. Insome embodiments a biomarker panel comprises 5 or more proteins, wherein5 of the proteins comprises AACT, CATD, CEA, CO3, and MIF. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, CO3, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, CO3, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, CO9, and MIF. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, CO9, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, CO9, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, MIF, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, MIF, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CEA, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO3, CO9, and MIF. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO3, CO9, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO3, CO9, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO3, MIF, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO3, MIF, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO3, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO9, MIF, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO9, MIF, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, CO9, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CATD, MIF, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises AACT, CEA, CO3, CO9, and MIF. In some embodimentsa biomarker panel comprises 5 or more proteins, wherein 5 of theproteins comprises AACT, CEA, CO3, CO9, and PSGL. In some embodiments abiomarker panel comprises 5 or more proteins, wherein 5 of the proteinscomprises AACT, CEA, CO3, CO9, and SEPR. In some embodiments a biomarkerpanel comprises 5 or more proteins, wherein 5 of the proteins comprisesAACT, CEA, CO3, MIF, and PSGL. In some embodiments a biomarker panelcomprises 5 or more proteins, wherein 5 of the proteins comprises AACT,CEA, CO3, MIF, and SEPR. In some embodiments a biomarker panel comprises5 or more proteins, wherein 5 of the proteins comprises AACT, CEA, CO3,PSGL, and SEPR. In some embodiments a biomarker panel comprises 5 ormore proteins, wherein 5 of the proteins comprises AACT, CEA, CO9, MIF,and PSGL. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CEA, CO9, MIF, andSEPR. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CEA, CO9, PSGL, andSEPR. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CEA, MIF, PSGL, andSEPR. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CO3, CO9, MIF, andPSGL. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CO3, CO9, MIF, andSEPR. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CO3, CO9, PSGL, andSEPR. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CO3, MIF, PSGL, andSEPR. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises AACT, CO9, MIF, PSGL, andSEPR. In some embodiments a biomarker panel comprises 5 or moreproteins, wherein 5 of the proteins comprises CATD, CEA, CO3, CO9, andMIF. In some embodiments a biomarker panel comprises 5 or more proteins,wherein 5 of the proteins comprises CATD, CEA, CO3, CO9, and PSGL. Insome embodiments a biomarker panel comprises 5 or more proteins, wherein5 of the proteins comprises CATD, CEA, CO3, CO9, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CEA, CO3, MIF, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CEA, CO3, MIF, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CEA, CO3, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CEA, CO9, MIF, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CEA, CO9, MIF, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CEA, CO9, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CEA, MIF, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CO3, CO9, MIF, and PSGL. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CO3, CO9, MIF, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CO3, CO9, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CO3, MIF, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CATD, CO9, MIF, PSGL, and SEPR. In someembodiments a biomarker panel comprises 5 or more proteins, wherein 5 ofthe proteins comprises CEA, CO3, CO9, MIF, and PSGL. In some embodimentsa biomarker panel comprises 5 or more proteins, wherein 5 of theproteins comprises CEA, CO3, CO9, MIF, and SEPR. In some embodiments abiomarker panel comprises 5 or more proteins, wherein 5 of the proteinscomprises CEA, CO3, CO9, PSGL, and SEPR. In some embodiments a biomarkerpanel comprises 5 or more proteins, wherein 5 of the proteins comprisesCEA, CO3, MIF, PSGL, and SEPR. In some embodiments a biomarker panelcomprises 5 or more proteins, wherein 5 of the proteins comprises CEA,CO9, MIF, PSGL, and SEPR. In some embodiments a biomarker panelcomprises 5 or more proteins, wherein 5 of the proteins comprises CO3,CO9, MIF, PSGL, and SEPR.

In some embodiments a biomarker panel comprises 4 or more proteins,wherein 4 of the proteins comprises: AACT, CATD, CEA, CO3; AACT, CATD,CEA, CO9; AACT, CATD, CEA, MIF; AACT, CATD, CEA, PSGL; AACT, CATD, CEA,SEPR; AACT, CATD, CO3, CO9; AACT, CATD, CO3, MIF; AACT, CATD, CO3, PSGL;AACT, CATD, CO3, SEPR; AACT, CATD, CO9, MIF; AACT, CATD, CO9, PSGL;AACT, CATD, CO9, SEPR; AACT, CATD, MIF, PSGL; AACT, CATD, MIF, SEPR;AACT, CATD, PSGL, SEPR; AACT, CEA, CO3, CO9; AACT, CEA, CO3, MIF; AACT,CEA, CO3, PSGL; AACT, CEA, CO3, SEPR; AACT, CEA, CO9, MIF; AACT, CEA,CO9, PSGL; AACT, CEA, CO9, SEPR; AACT, CEA, MIF, PSGL; AACT, CEA, MIF,SEPR; AACT, CEA, PSGL, SEPR; AACT, CO3, CO9, MIF; AACT, CO3, CO9, PSGL;AACT, CO3, CO9, SEPR; AACT, CO3, MIF, PSGL; AACT, CO3, MIF, SEPR; AACT,CO3, PSGL, SEPR; AACT, CO9, MIF, PSGL; AACT, CO9, MIF, SEPR; AACT, CO9,PSGL, SEPR; AACT, MIF, PSGL, SEPR; CATD, CEA, CO3, CO9; CATD, CEA, CO3,MIF; CATD, CEA, CO3, PSGL; CATD, CEA, CO3, SEPR; CATD, CEA, CO9, MIF;CATD, CEA, CO9, PSGL; CATD, CEA, CO9, SEPR; CATD, CEA, MIF, PSGL; CATD,CEA, MIF, SEPR; CATD, CEA, PSGL, SEPR; CATD, CO3, CO9, MIF; CATD, CO3,CO9, PSGL; CATD, CO3, CO9, SEPR; CATD, CO3, MIF, PSGL; CATD, CO3, MIF,SEPR; CATD, CO3, PSGL, SEPR; CATD, CO9, MIF, PSGL; CATD, CO9, MIF, SEPR;CATD, CO9, PSGL, SEPR; CATD, MIF, PSGL, SEPR; CEA, CO3, CO9, MIF; CEA,CO3, CO9, PSGL; CEA, CO3, CO9, SEPR; CEA, CO3, MIF, PSGL; CEA, CO3, MIF,SEPR; CEA, CO3, PSGL, SEPR; CEA, CO9, MIF, PSGL; CEA, CO9, MIF, SEPR;CEA, CO9, PSGL, SEPR; CEA, MIF, PSGL, SEPR; CO3, CO9, MIF, PSGL; CO3,CO9, MIF, SEPR; CO3, CO9, PSGL, SEPR; CO3, MIF, PSGL, SEPR; CO9, MIF,PSGL, SEPR.

In some embodiments a bio-marker panel comprises 3 or more proteins,wherein 3 of the proteins comprises: AACT, CATD, CEA; AACT, CATD, CO3;AACT, CATD, CO9; AACT, CATD, MIF; AACT, CATD, PSGL; AACT, CATD, SEPR;AACT, CEA, CO3; AACT, CEA, CO9; AACT, CEA, MIF; AACT, CEA, PSGL; AACT,CEA, SEPR; AACT, CO3, CO9; AACT, CO3, MIF; AACT, CO3, PSGL; AACT, CO3,SEPR; AACT, CO9, MIF; AACT, CO9, PSGL; AACT, CO9, SEPR; AACT, MIF, PSGL;AACT, MIF, SEPR; AACT, PSGL, SEPR; CATD, CEA, CO3; CATD, CEA, CO9; CATD,CEA, MIF; CATD, CEA, PSGL; CATD, CEA, SEPR; CATD, CO3, CO9; CATD, CO3,MIF; CATD, CO3, PSGL; CATD, CO3, SEPR; CATD, CO9, MIF; CATD, CO9, PSGL;CATD, CO9, SEPR; CATD, MIF, PSGL; CATD, MIF, SEPR; CATD, PSGL, SEPR;CEA, CO3, CO9; CEA, CO3, MIF; CEA, CO3, PSGL; CEA, CO3, SEPR; CEA, CO9,MIF; CEA, CO9, PSGL; CEA, CO9, SEPR; CEA, MIF, PSGL; CEA, MIF, SEPR;CEA, PSGL, SEPR; CO3, CO9, MIF; CO3, CO9, PSGL; CO3, CO9, SEPR; CO3,MIF, PSGL; CO3, MIF, SEPR; CO3, PSGL, SEPR; CO9, MIF, PSGL; CO9, MIF,SEPR; CO9, PSGL, SEPR; MIF, PSGL, SEPR.

In some embodiments a bio-marker panel comprises 2 or more proteins,wherein 2 of the proteins comprises: AACT, CATD; AACT, CEA; AACT, CO3;AACT, CO9; AACT, MIF; AACT, PSGL; AACT, SEPR; CATD, CEA; CATD, CO3;CATD, CO9; CATD, MIF; CATD, PSGL; CATD, SEPR; CEA, CO3; CEA, CO9; CEA,MIF; CEA, PSGL; CEA, SEPR; CO3, CO9; CO3, MIF; CO3, PSGL; CO3, SEPR;CO9, MIF; CO9, PSGL; CO9, SEPR; MIF, PSGL; MIF, SEPR; PSGL, SEPR.

The biomarker panels of Table 3 correspond to a number of embodiments ofthe lead panel, as recited below. Similar variants of other lead panelsin the disclosure are contemplated and apparent to one of skill in theart such that they do not warrant redundant recitation.

In some embodiments, a diagnostic method provided herein comprisesmeasuring in the biological sample a biomarker panel comprising at least7, at least 6, at least 5, at least 4, at least 3, or at least 2 of:A1AG1, A1AT, CATD, CEA, CO9, OSTP, and SEPR. In some embodiments, adiagnostic method provided herein comprises measuring in the biologicalsample a biomarker panel comprising at least 17, at least 16, at least15, at least 14, at least 13, at least 12, at least 11, at least 10, atleast 9, at least 8, at least 7, at least 6, at least 5, at least 4, atleast 3, or at least 2 of: A1AG1, A1AT, APOA1, CATD, CEA, CLUS, CO3,CO9, FGB, FIBG, GARS, GELS, MIF, PRDX1, PSGL, SBP1, and SEPR. In someembodiments, a diagnostic method provided herein comprises measuring inthe biological sample a biomarker panel consisting at least 7, at least6, at least 5, at least 4, at least 3, or at least 2 of: A1AG1, A1AT,CATD, CEA, CO9, GARS, and SEPR. In some embodiments, a diagnostic methodprovided herein comprises measuring in the biological sample a biomarkerpanel comprising at least 13, at least 12, at least 11, at least 10, atleast 9, at least 8, at least 7, at least 6, at least 5, at least 4, atleast 3, or at least 2 of: A1AG1, A1AT, AACT, CATD, CEA, CO9, CRP, GARS,GELS, S10A8, S10A9, SAA1, and SEPR. In some embodiments, a diagnosticmethod provided herein comprises measuring in the biological sample abiomarker panel comprising at least 8, at least 7, at least 6, at least5, at least 4, at least 3, or at least 2 of: CATD, CEA, CO3, CO9, GARS,GELS, SEPR, and TFRC. In some embodiments, a diagnostic method providedherein comprises measuring in the biological sample a biomarker panelcomprising at least 5, at least 4, at least 3, or at least 2 of: CATD,CEA, AACT, CO9, and SEPR. In some embodiments, a diagnostic methodprovided herein comprises measuring in the biological sample a biomarkerpanel comprising at least 6, at least 5, at least 4, at least 3, or atleast 2 of: A1AT, CATD, CEA, GARS, GELS, and SEPR. In some embodiments,a diagnostic method provided herein comprises measuring in thebiological sample a biomarker panel comprising at least 18, of at least17, at least 16, at least 15, at least 14, at least 13, at least 12, atleast 11, at least 10, at least 9, at least 8, at least 7, at least 6,at least 5, at least 4, at least 3, or at least 2 of: A1AG1, A1AT,APOA1, CATD, CEA, CLUS, CO3, CO9, FGB, FIBG, GARS, GELS, HPT, MIF,PRDX1, PSGL, SBP1, and SEPR. In some embodiments, a diagnostic methodprovided herein comprises measuring in the biological sample a biomarkerpanel comprising at least 8, at least 7, at least 6, at least 5, atleast 4, at least 3, or at least 2 of: A1AG1, A1AT, CATD, CEA, CO9,FIBG, GELS, and SEPR. In some embodiments, a diagnostic method providedherein comprises measuring in the biological sample a biomarker panelcomprising at least 3, or at least 2 of: CATD, CEA, and SEPR. In someembodiments, a diagnostic method provided herein comprises measuring inthe biological sample a biomarker panel consisting at least 8, at least7, at least 6, at least 5, at least 4, at least 3, or at least 2 of:CATD, CEA, CO3, CO9, MIF, PSGL, SEPR, and TFRC. In some embodiments, adiagnostic method provided herein comprises measuring in the biologicalsample a biomarker panel consisting at least 7, at least 6, at least 5,at least 4, at least 3, or at least 2 of: A1AG1, CATD, CEA, CO3, CO9,GELS, and SEPR. Furthermore, the group of biomarkers in this example canin some cases additionally comprise polypeptides with thecharacteristics found in Table 1.

In some embodiments, a biomarker panel comprises at least 3 or at least2 of CATD, CLAUS, GDF15, and SAA1. In some embodiments a panelcomprising CATD, CLAUS, GDF15, and SAA1 is designated for advancedadenoma detection. In some embodiments, a diagnostic method providedherein comprises measuring in the biological sample a biomarker panelcomprising A1AG1, A1AT, APOA1, CATD, CEA, CLUS, CO3, CO9, FGB, FIBG,GARS, GELS, MIF, PRDX1, PSGL, SBP1, and SEPR.

Biomarker Panel Assessment

Some methods described herein comprise comparing the amount of each ofthe at least two biomarkers in the biological sample to a referenceamount of each of the at least two biomarkers. Some methods hereincomprise comparing the profile of the biomarker panel in a subject to areference profile of the biomarker panel. The reference amount is insome cases an amount of the biomarker in a control subject. Thereference profile of the biomarker panel is in some cases a biomarkerprofile of a control subject. The control subject is in some cases asubject having a known diagnosis. For example, the control subject canbe a negative control subject. The negative control subject can be asubject that does not have advanced colorectal adenoma. The negativecontrol subject can be a subject that does not have CRC. The negativecontrol subject can be a subject that does not have a colon polyp. Forother example, the control subject can be a positive control subject.The positive control subject can be a subject having a confirmeddiagnosis of advanced colorectal adenoma. The positive control subjectcan be a subject having a confirmed diagnosis of CRC. The positivecontrol subject can be a subject having a confirmed diagnosis of anystage of CRC (for example, Stage 0, Stage I, Stage II, Stage IIA, StageIIB, Stage IIC, Stage III, Stage IIIA, Stage IIIB, Stage IIIC, Stage IV,Stage IVA, or Stage IVB). The reference amount can be a predeterminedlevel of the biomarker, wherein the predetermined level is set basedupon a measured amount of the biomarker in a control subject.

Some reference biomarker panel levels comprises average values for anumber of individuals having a common condition status, such as 10individuals free of CRC or AA, or 10 individuals of a known stage of CRCor a known AA status. Alternately, in some cases references comprise aset of protein accumulation levels, and age in some embodiments, thatcorrespond to a set of individuals of known CRC or AA status. In thesecases, levels are not averaged; rather, a patient's levels are comparedto each set of accumulation levels of each standard or referenceindividual in the set, and a determination is made if the patient'saccumulation levels do not differ significantly from those of at leastone reference set. In some cases the reference set comprises individualsof known cancer-free status, while in some cases the reference setcomprises individuals of known CRC or AA stage status, such as Stage 0,Stage I, Stage II, Stage 11A, Stage IIB, Stage TIC, Stage III, Stage111A, Stage IIIB, Stage IIIC, Stage IV, Stage IVA, or Stage IVB. In somecases a patient is categorized as having a condition if the patient'spanel accumulation levels match or do not differ significantly fromthose of a reference. In some cases a patient is categorized as nothaving a condition if a patient's panel accumulation levels differsignificantly from those of a reference.

In some cases, comparing comprises determining a difference between anamount of the biomarker in the biological sample obtained from thesubject and the reference amount of the biomarker. The method comprises,in some cases, detecting a presence or absence of at least one ofadvanced colorectal adenoma and CRC based upon a deviation (for example,measured difference) of the amount of at least one of the measuredbiomarkers in the biological sample obtained from the subject ascompared to a reference amount of the at least one measured biomarkers.In some cases, the method comprises detecting a presence of at least oneof advanced colorectal adenoma and CRC if the deviation of the amount ofthe at least one measured biomarker from the biological sample obtainedfrom the subject as compared to a positive reference value (for example,an amount of the measured biomarker from a positive control subject) islow. In other cases, the method comprises detecting a presence of atleast one of advanced colorectal adenoma and CRC if the deviation of theamount of the at least one measured biomarker from the biological sampleobtained from the subject as compared to a negative reference value (forexample, measured from a negative control subject) is high. In somecases, the method comprises detecting an absence of at least one ofadvanced colorectal adenoma and CRC if the deviation of the amount ofthe at least one measured biomarker from the biological sample obtainedfrom the subject as compared to a positive reference value (for example,measured from a positive control subject) is high. In some examples, themethod comprises detecting an absence of at least one of advancedcolorectal adenoma and CRC if the deviation of the amount of the atleast one measured biomarker from the biological sample obtained fromthe subject as compared to a negative reference value (for example,measured from a negative control subject) is low. In some cases,detection of a presence or absence of at least one of advancedcolorectal adenoma and CRC can be based upon a clinical outcome scoreproduced by an algorithm described herein. In some cases, the methodcomprises detection of a presence or absence of colorectal cancer basedupon a classifier that divides a feature space into feature values thatare predictive of the presence of colorectal cancer and feature valuesthat are predictive of the absence of colorectal cancer. In some cases,the method comprises classifying a subject's colorectal cancer status as“undetermined” (e.g., “no call”) in order to reduce false positivesand/or false negatives. In some cases, patients with an undeterminedcolorectal cancer status are retested at a later point. The algorithmcan be used for assessing the deviation between an amount of a measuredbiomarker in the biological sample obtained from the subject and areference amount of the biomarker.

In some cases, a classifier is used to determine the colorectal cancerstatus of a subject. For example, given N measurements as inputs intothe classifier (e.g., the biomarkers comprising proteins and the age ofthe subject), the subject can be represented as a point in anN-dimensional space wherein each axis is a measurement. In some cases,the classifier defines an N−1)-dimensional shape that divides theN-dimensional space into two or more categories. In some cases, the twocategories are a subject with cancer and a subject without cancer. Insome cases there are three categories. In some cases the categories area subject with cancer, a subject without cancer, and a no-cal 1 regionwhere the cancer status of the subject cannot be reliably determined. Insome cases, the classifier allows ‘shifting’ cutoffs for particularproteins. For example, consider a classifier defined by the boundaryy=1/x, where x and y are both greater than zero, and each of the twoaxes is the accumulation level of a protein indicative of cancer status.In such a case, all the subjects whose protein accumulation levels fallbeneath the boundary (e.g., [0, 0], [2, 0.3], etc. . . . ) areclassified as not having the condition, whereas any subject whoseprotein accumulation levels lie above the boundary are classified ashaving the condition. If the x-axis protein has a value of 1, then inthis example the y-axis protein must be more than one to result in acancer diagnosis. However, if the x-axis protein has a value of 10, thenthe y-axis protein need only have a value more than 0.1 to result in acancer diagnosis. This example can be extrapolated to an N-dimensionalshape using an (N−1)-dimensional shape as the classifier.

The intrinsic performance of a particular classification model dependson the distributions and separation of model scores for the two classes.With the rare exception of perfect class separation, most classificationmodels make mistakes because of class overlap across the range ofclassifier scores. For example, such an overlap may occur near themiddle of the score range where the probability of being in one class orthe other is close to 50%.

Within such an overlap region, it is sometimes advantageous to add athird class to the final set of classification calls. The third classoptionally indicates the uncertainty of a call in this score region.This is implemented, for example, by defining an indeterminate region ofclassification scores. Samples with scores in this region are given an“indeterminate” or “no call” test result. Samples with scores above orbelow this region would be given standard positive or negative testresults depending on their positions relative to the test cutoff. Insome cases, the “no call” rate, or the frequency with which samples fallinto the “no call” region, is about 1%, about 2%, about 3%, about 4%,about 5%, about 10%, about 15%, or about 20%. In particular, the “nocall” rate can be about 10%. The benefit of adding an indeterminateregion to a classification model is that classification performance canimprove for samples outside of the indeterminate region, i.e. mistakesare less likely for the remaining positive and negative tests. However,if the indeterminate range is too large, there may be too manyindeterminate results, and the value of the test may be put intoquestion.

Classifier Construction

Reference classifiers are readily constructed by one of skill in the artusing any number of available technologies. Reference classifiers are,for example, generated by assaying panel levels for a plurality ofsamples, such as blood sample, obtained from individuals of knowncolorectal health status. As many as 1000 samples or more, comprisingsamples obtained from individuals known or later confirmed to havecolorectal cancer or known or later confirmed not to have colorectalcancer, as assayed as to their biomarker panel levels. Age, anon-protein biomarker constituent of some panels, is also recorded foreach individual at the time of sample collection.

In some cases, the biomarker panel levels for each sample are usedindividually as a reference panel level for comparison so as to classifyan individual's biomarker panel level as indicative of a healthycolorectal health status or a colorectal health issue warranting furtherinvestigation. A panel level to be classified is compared to thepositive and the negative biomarker panel levels, and the outcome asjudged by, for example, the number samples of each category from whichthe testing individual's panel level does not differ significantly.

Alternately, a classifier is assembled from the collection of biomarkerpanel levels. Classifier assembly is well known to those of skill in theart. Machine learning models, in particular, are useful in assembling aclassifier from a set of panel levels obtained from samples of knowncolorectal health status. Machine learning models are readilyconstructed, for example, using any number of statistical programmingprogramming languages such as R, scripting languages such as Python andassociated machine learning packages, data mining software such as Wekaor Java, Mathematica, Matlab or SAS.

Implementation of Classifiers in Colorectal Health Assessment

In practicing any of the methods described herein, comparing optionallycomprises determining a difference between a biomarker profile of asubject to a reference biomarker profile. The method can, for example,comprise detecting a presence or absence of at least one of advancedcolorectal adenoma and CRC based upon a deviation (for example, measureddifference) of the biomarker profile of the subject as compared to areference biomarker profile. For example, some methods comprisedetecting a presence of at least one of advanced colorectal adenoma andCRC if the deviation of the biomarker profile of the subject as comparedto a positive reference biomarker profile (for example, a biomarkerprofile based upon measurements of panel biomarkers from a positivecontrol subject) is low. As an additional example, some methods comprisedetecting a presence of at least one of advanced colorectal adenoma andCRC if the deviation of the biomarker profile of the subject as comparedto a negative reference biomarker profile (for example, a biomarkerprofile based upon measurements of panel biomarkers from a negativecontrol subject) is high. In some cases, the method comprises detectingan absence of at least one of advanced colorectal adenoma and CRC if thedeviation of the biomarker profile of the subject as compared to apositive reference biomarker profile is high. In some examples, themethod comprises detecting an absence of at least one of advancedcolorectal adenoma and CRC if the deviation of the biomarker profile ofthe subject as compared to a negative reference biomarker profile islow. In some cases, detection of a presence or absence of at least oneof advanced colorectal adenoma and CRC can be based upon a clinicaloutcome score produced by an algorithm described herein. The algorithmcan be used for assessing the deviation between the biomarker profile ofthe subject to a reference biomarker profile.

Some methods comprise detecting a presence or absence of an advancedcolorectal adenoma in the subject in some cases. The advanced colorectaladenoma can be a colorectal advanced colorectal adenoma. The methodsdescribed herein are be used to detect a presence or absence of anadvanced colorectal adenoma of any size, such as an advanced adenomahaving a dimension that is greater than 1 cm. The methods describedherein are used to detect a presence or absence of an advancedcolorectal adenoma of villous, serrated, sessile or non-pedunculatedcharacter.

In some cases, a diagnostic method provided herein comprises measuring abiomarker panel comprising at least five biomarkers in the biologicalsample, wherein the at least three biomarkers comprise AACT, CATD, CEA,CO3, CO9, MIF, PSGL, and SEPR. In some cases, the method comprisesproviding a positive diagnosis of advanced colorectal adenoma if adeviation in the panel level of a panel comprising AACT, CATD, CEA, CO3,CO9, MIF, PSGL, and SEPR in the biological sample obtained from thesubject as compared to a positive reference value is low. In some cases,the method comprises providing a positive diagnosis of advancedcolorectal adenoma if a deviation in the panel level of a panelcomprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR in thebiological sample obtained from the subject as compared to a negativereference value is high. In some cases, the method comprises providing apositive diagnosis of advanced colorectal adenoma if a deviation in thepanel level of a panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL,and SEPR in the biological sample obtained from the subject as comparedto a positive reference value is high. In some cases, the methodcomprises providing a positive diagnosis of advanced colorectal adenomaif a deviation in the panel level of a panel comprising AACT, CATD, CEA,CO3, CO9, MIF, PSGL, and SEPR in the biological sample obtained from thesubject as compared to a negative reference value is low.

Methods, compositions, kits and systems disclosed herein detect advancedcolorectal adenoma with a sensitivity greater than 70%, greater than75%, greater than 80%, greater than 85%, greater than 90%, greater than95%, greater than 96%, greater than 97%, greater than 98%, greater than99%, or about 100%. Such diagnostic method detects advanced colorectaladenoma with a sensitivity that is between about 50%-100%, between about60%-100%, between about 70%-100%, between about 80%-100%, or betweenabout 90-100%. Such diagnostic methods detect advanced colorectaladenoma with a sensitivity of at least 70%, of at least 75%, of at least80%, of at least 85%, of at least 90%, of at least 95%, of at least 96%,of at least 97%, of at least 98%, of at least 99%, or about 100%. Suchdiagnostic methods detect advanced colorectal adenoma with a specificitythat is between about 50%-100%, between about 60%-100%, between about70%-100%, between about 80%-100%, or between about 90-100%. Inparticular cases, such diagnostic method detects advanced colorectaladenoma with a sensitivity and a specificity that is 50% or greater, 60%or greater, 70% or greater, 75% or greater, 80% or greater, 85% orgreater, 90% or greater. In particular cases, such diagnostic detectsadvanced colorectal adenoma with a sensitivity and a specificity that isbetween about 50%-100%, between about 60%-100%, between about 70%-100%,between about 80%-100%, or between about 90-100%.

In some cases, a panel comprises a ratio of a level of a first biomarkerto a level of a second biomarker. Accordingly, in some cases, adiagnostic method provided herein comprises determining a ratio of alevel of the first biomarker to a level of the second biomarker in thebiological sample obtained from the subject. In some cases, the methodcomprises providing a positive diagnosis of CRC if a deviation in theratio of the first biomarker to the second biomarker in the biologicalsample obtained from the subject as compared to a positive referencevalue is low. In some cases, the method comprises providing a positivediagnosis of CRC if a deviation in the ratio of the first biomarker tothe second biomarker in the biological sample obtained from the subjectas compared to a negative reference value is high. In some cases, themethod comprises providing a positive diagnosis of if a deviation in theratio of the first biomarker to the second biomarker in the biologicalsample obtained from the subject as compared to a positive referencevalue is high. In some cases, the method comprises providing a positivediagnosis of CRC if a deviation in the ratio of the first biomarker tothe second biomarker in the biological sample obtained from the subjectas compared to a negative reference value is low.

Diagnostic methods described herein for detection of CRC in a subjectdetects CRC with a sensitivity greater than 75%, greater than 80%,greater than 85%, greater than 90%, greater than 95%, greater than 96%,greater than 97%, greater than 98%, greater than 99%, or about 100%.Such diagnostic methods detect CRC with a sensitivity that is betweenabout 70%-100%, between about 80%-100%, or between about 90-100%. Suchdiagnostic methods detect CRC with a specificity greater than 70%,greater than 75%, greater than 80%, greater than 85%, greater than 90%,greater than 95%, greater than 96%, greater than 97%, greater than 98%,greater than 99%, or about 100%. Such diagnostic methods detect CRC witha specificity that is between about 50%-100%, between about 60%-100%,between about 70%-100%, between about 80%-100%, or between about90-100%. In particular embodiments, such diagnostic methods detect CRCwith a sensitivity and a specificity that is 50% or greater, 60% orgreater, 70% or greater, 75% or greater, 80% or greater, 85% or greater,90% or greater. In particular embodiments, such diagnostic methodsdetect CRC with a sensitivity and a specificity that is between about50%-100%, between about 60%-100%, between about 70%-100%, between about80%-100%, or between about 90-100%.

The overall performance of a classifier is assessed in some cases viathe AUC of the ROC as reported herein. An ROC considers the performanceof the classifier at all possible model score cutoff points. However,when a classification decision needs to be made (e.g., is this patientsick or healthy?), a cutoff point is used to define the two groups.Classification scores at or above the cutoff point are assessed aspositive (or sick) while points below are assessed as negative (orhealthy) in various embodiments.

For some classification models disclosed herein, a classification scorecutoff point is established by selecting the point of maximum accuracyon the validation ROC. The point of maximum accuracy on an ROC is thecutoff point or points for which the total number of correctclassification calls is maximized. Here, the positive and negativeclassification calls are weighted equally. In cases where multiplemaximum accuracy points are present on a given ROC, the point with theassociated maximum sensitivity is selected in some cases.

Algorithm-Based Methods

Methods, compositions, kits, and systems described herein utilize analgorithm-based diagnostic assay for predicting a presence or absence ofat least one of: advanced colorectal adenoma and CRC in a subject.Expression level of one or more protein biomarker, and optionally one ormore subject characteristics, such as, for example, age, weight, gender,medical history, risk factors, or family history are used alone orarranged into functional subsets to calculate a quantitative score thatis used to predict the likelihood of a presence or absence of at leastone of advanced colorectal adenoma and CRC. Although lead embodimentsherein focus upon biomarker panels that are predominantly protein orpolypeptide panels, the measurements of any of the biomarker panels maycomprise protein and non-protein components such as RNA, DNA, organicmetabolites, or inorganic molecules or metabolites (e.g. iron,magnesium, selenium, calcium, or others).

The algorithm-based assay and associated information provided by thepractice of any of the methods described herein can facilitate optimaltreatment decision-making in subjects. For example, such a clinical toolcan enable a physician or caretaker to identify patients who have a lowlikelihood of having an advanced colorectal adenoma or carcinoma andtherefore would not need treatment, or increased monitoring for advancedcolorectal adenoma or CRC, or who have a high likelihood of having anadvanced colorectal adenoma or CRC and therefore would need treatment orincreased monitoring of said advanced colorectal adenoma or CRC.

A quantitative score is determined by the application of a specificalgorithm in some cases. The algorithm used to calculate thequantitative score in the methods disclosed herein may group theexpression level values of a biomarker or groups of biomarkers. Theformation of a particular group of biomarkers, in addition, canfacilitate the mathematical weighting of the contribution of variousexpression levels of biomarker or biomarker subsets (for exampleclassifier) to the quantitative score. Described herein are exemplaryalgorithms for calculating the quantitative scores.

Exemplary biomarkers and, when applicable their human amino acidsequences, are listed in Tables 1 and 3. Biomarkers may comprise fulllength molecules of the polypeptide sequences of Table 3, as well asuniquely identifiable fragments of the polypeptide sequences of Table 1.Markers can be but do not need to be full length to be informative. Inmany cases, so long as a fragment is uniquely identifiable as beingderived from or representing a polypeptide of Table 3, it is informativefor purposes herein.

Exemplary Subjects

Biological samples are collected from a number of eligible subjects,such as subjects who want to determine their likelihood of having atleast one of advanced colorectal adenoma and CRC. The subject is in somecases healthy and asymptomatic. The subject's age is not constrained.For example, the subject is between the ages of 0 to about 30 years,about 20 to about 50 years, or about 40 or older. In various cases, thesubject is healthy, asymptomatic and between the ages of 0-30 years,20-50 years, or 40 or older. The subject is at least 30 years of age, atleast 40 years of age, or at least 50 years of age. The subject is lessthan 50 years of age, less than 40 years of age, or less than 30 yearsof age. In various examples, the subject is healthy and asymptomatic. Invarious examples, the subject has no family history of at least one of:CRC, adenoma, and polyps. In various examples, the subject has not had acolonoscopy, sigmoidoscopy, or colon tissue biopsy. In various examples,the subject is healthy and asymptomatic and has not received acolonoscopy, sigmoidoscopy, or colon tissue biopsy. In some cases, thesubject has not received a colonoscopy, sigmoidoscopy, or colon tissuebiopsy and has one or more of: a symptom of CRC, a family history ofCRC, and a risk factor for CRC. In some cases, a biological sample canbe obtained from a subject during routine examination, or to establishbaseline levels of the biomarkers. In some cases, a subject has nosymptoms for colorectal carcinoma, has no family history for colorectalcarcinoma, has no recognized risk factors for colorectal carcinoma.

In some cases, a subject presents at least one of: a symptom forcolorectal carcinoma, a family history for colorectal carcinoma, and arecognized risk factor for colorectal carcinoma. In some cases, asubject is identified through screening assays (for example, fecaloccult blood testing or sigmoidoscopy) or rectal digital exam or rigidor flexible colonoscopy or CT scan or other x-ray techniques as being athigh risk for or having CRC. For example, one or more methods describedherein are applied to a subject undergoing treatment for CRC, todetermine the effectiveness of the therapy or treatment they arereceiving.

Exemplary Biological Samples

Biological samples in some exemplary embodiments are circulating bloodsamples or are samples obtained from the vein or artery of anindividual. Samples are optionally processed, so as to isolate plasma,circulating free proteins, or a whole protein fraction from the bloodsample. Samples are often treated to facilitate storage or to allowshipment at room temperature, although in preferred embodiments samplesare shipped frozen, for example with or on dry ice, to preserve thesamples for analysis at a processing center separate from aphlebotomist's office.

As a representative sample collection protocol, blood samples for serum,EDTA plasma, citrate plasma and buffy-coats are collected with lighttournique from an antecubital vein using endotoxin-, deoxyribonuclease(DNAse-) and ribonuclease (RNAse-) free collection and handlingequipment, collection tubes and storage vials from Becton-Dickinson,Franklin Lakes, N.J., USA and Almeco A/S, Esbjerg, Denmark. The bloodsamples are centrifuged at 3,000×G for 10 mins at 21° C. and serum andplasma are immediately separated from the red cell and buffy-coatlayers. Contamination by white cells and platelets is reduced by leaving0.5 cm of untouched serum or plasma above the buffy-coat, which isseparately transferred for freezing. All separated samples are markedwith unique barcodes for storage identification, which is performedusing the FreezerWorks®, Seattle, Wash., USA tracking system. Separatedsamples are frozen at −80° C. under continuous electronic surveillance.The entire procedure is completed within 2 hours of initial sample draw.

Additional biological samples include one or more of, but are notlimited to: urine, stool, tears, whole blood, serum, plasma, bloodconstituent, bone marrow, tissue, cells, organs, saliva, cheek swab,lymph fluid, cerebrospinal fluid, lesion exudates and other fluidsproduced by the body. The biological sample is in some cases a solidbiological sample, for example, a tissue biopsy. The biopsy can befixed, paraffin embedded, or fresh. In many embodiments herein, apreferred sample is a blood sample drawn from a vein or artery of anindividual, or a processed product thereof.

Biological samples are optionally processed using any approach known inthe art or otherwise described herein to facilitate measurement of oneor more biomarkers as described herein. Sample preparation operationscomprise, for example, extraction and/or isolation of intracellularmaterial from a cell or tissue such as the extraction of nucleic acids,protein, or other macromolecules. Sample preparation which can be usedwith the methods of disclosure include but are not limited to,centrifugation, affinity chromatography, magnetic separation,immunoassay, nucleic acid assay, receptor-based assay, cytometric assay,colorimetric assay, enzymatic assay, electrophoretic assay,electrochemical assay, spectroscopic assay, chromatographic assay,microscopic assay, topographic assay, calorimetric assay, radioisotopeassay, protein synthesis assay, histological assay, culture assay, andcombinations thereof.

Sample preparation optionally includes dilution by an appropriatesolvent and amount to ensure the appropriate range of concentrationlevel is detected by a given assay.

Accessing the nucleic acids and macromolecules from the intercellularspace of the sample is performed by either physical, chemical methods,or a combination of both. In some applications of the methods, followingthe isolation of the crude extract, it will often be desirable toseparate the nucleic acids, proteins, cell membrane particles, and thelike. In some applications of the methods it will be desirable to keepthe nucleic acids with its proteins, and cell membrane particles.

In some applications of the methods provided herein, nucleic acids andproteins are extracted from a biological sample prior to analysis usingmethods of the disclosure. Extraction is accomplished, for examplethrough use of detergent lysates, sonication, or vortexing using glassbeads.

In some applications, molecules can be isolated using any techniquesuitable in the art including, but not limited to, techniques usinggradient centrifugation (for example, cesium chloride gradients, sucrosegradients, glucose gradients, or other gradients), centrifugationprotocols, boiling, purification kits, and the use of liquid extractionwith agent extraction methods such as methods using Trizol or DNAzol.

In some cases, the sample is partially prepared at a separate locationprior to being sent for analysis. For example, a phlebotomist draws ablood sample at a clinic or hospital. The sample can be partiallyprocessed, for example, by placing in anticoagulant-treated tubes andcentrifuging to produce plasma. The partially processed sample, such asthe plasma, is then shipped (e.g., mailed on ice or in preservative atroom temperature) to a separate facility where any of the methodsdisclosed herein can be performed to determine a biomarker panel leveland/or CRC or advanced adenoma health status.

Samples are prepared according to standard biological sample preparationdepending on the desired detection method. For example, for massspectrometry detection, biological samples obtained from a patient maybe centrifuged, filtered, processed by immunoaffinity column, separatedinto fractions, partially digested, and combinations thereof. Variousfractions may be resuspended in appropriate carrier such as buffer orother type of loading solution for detection and analysis, includingLCMS loading buffer.

Biomarker Assessment

The present disclosure provides for methods for measuring one or morebiomarker panels in biological samples. Any suitable method can be usedto detect one or more of the biomarkers of any of the panels describedherein.

In some cases, only values falling within specific ranges are reported.For example, in some cases, assayed protein concentrations below a givencutoff indicate a failed assay. Exemplary acceptable ranges forparticular biomarkers are disclosed in Table 4.

TABLE 4 Exemplary acceptable ranges for biomarkers of interest. ProteinLow High Units AACT 62.5 4000 μg/ml CATD 62.5 1000 ng/ml CEA 3 120 ng/mlCLUS 30 480 μg/ml CO3 117.25 7500 μg/ml CO9 4.68 300 μg/ml GDF15 187.212000 pg/ml MIF 3.13 100 ng/ml PSGL 93.75 1500 U/ml SAA1 18 144 μg/mlSEPR 10 160 ng/ml

Useful analyte capture agents used in practice of methods describedherein include but are not limited to antibodies, such as crude serumcontaining antibodies, purified antibodies, monoclonal antibodies,polyclonal antibodies, synthetic antibodies, antibody fragments (forexample, Fab fragments); antibody interacting agents, such as protein A,carbohydrate binding proteins, and other interactants; proteininteractants (for example avidin and its derivatives); peptides; andsmall chemical entities, such as enzyme substrates, cofactors, metalions/chelates, aptamers, and haptens. Antibodies may be modified orchemically treated to optimize binding to targets or solid surfaces (forexample biochips and columns).

Biomarkers are measured in some cases in a biological sample using animmunoassay. Some immunoassays use antibodies that specifically orinformatively bind to or recognize an antigen (for example site on aprotein or peptide, biomarker target). Some immunoassays include thesteps of contacting the biological sample using the antibody andallowing the antibody to form a complex of with the antigen in thesample, washing the sample and detecting the antibody-antigen complexwith a detection reagent. Antibodies that recognize the biomarkers maybe commercially available. An antibody that recognizes the biomarkerscan be generated by known methods of antibody production.

Immunoassays include indirect assays, wherein, for example, a second,labeled antibody can be used to detect bound marker-specific antibody.Exemplary detectable labels include magnetic beads (for example,DYNABEADS™), fluorescent dyes, radiolabels, enzymes (for example,horseradish peroxide, alkaline phosphatase and others commonly used),and calorimetric labels such as colloidal gold or colored glass orplastic beads. The biomarker in the sample can be measured using acompetition or inhibition assay wherein, for example, a monoclonalantibody which binds to a distinct epitope of the marker is incubatedsimultaneously with the mixture.

The conditions to detect an antigen using an immunoassay are dependenton the particular antibody used. Also, the incubation time can dependupon the assay format, marker, volume of solution, concentrations andthe like. Immunoassays can be carried out at room temperature, althoughthey can be conducted over a range of temperatures, such as from about 0degrees to about 40 degrees Celsius depending on the antibody used.

There are various types of immunoassay known in the art that as astarting basis can be used to tailor the assay for the detection of thebiomarkers of the present disclosure. Useful assays can include, forexample, an enzyme immune assay (EIA) such as enzyme-linkedimmunosorbent assay (ELISA). For example, if an antigen can be bound toa solid support or surface, it can be detected by reacting it with aspecific antibody and the antibody can be quantitated by reacting itwith either a secondary antibody or by incorporating a label directlyinto the primary antibody. Alternatively, an antibody can be bound to asolid surface and the antigen added. A second antibody that recognizes adistinct epitope on the antigen can then be added and detected. Suchassay can be referred to as a ‘sandwich assay’ and can be used to avoidproblems of high background or non-specific reactions. These types ofassays can be sensitive and reproducible enough to measure lowconcentrations of antigens in a biological sample.

Immunoassays are used to determine presence or absence of a marker in asample as well as the quantity of a marker in a sample. Methods formeasuring the amount of, or presence of, antibody-marker complex includebut are not limited to, fluorescence, luminescence, chemiluminescence,absorbance, reflectance, transmittance, birefringence or refractiveindex (for example, surface plasmon resonance, ellipsometry, a resonantmirror method, a grating coupler waveguide method or interferometry).Such reagents can be used with optical detection methods, such asvarious forms of microscopy, imaging methods and non-imaging methods.Electrochemical methods can include voltammetry and amperometry methods.Radio frequency methods can include multipolar resonance spectroscopy.

Measurement of biomarkers optionally involves use of an antibody.Antibodies that specifically bind to any of the biomarkers describedherein can be prepared using standard methods known in the art. Forexample polyclonal antibodies can be produced by injecting an antigeninto a mammal, such as a mouse, rat, rabbit, goat, sheep, or horse forlarge quantities of antibody. Blood isolated from these animals cancontain polyclonal antibodies—multiple antibodies that bind to the sameantigen. Alternatively, polyclonal antibodies can be produced byinjecting the antigen into chickens for generation of polyclonalantibodies in egg yolk. In addition, antibodies can be made tospecifically recognize modified forms for the biomarkers such as aphosphorylated form of the biomarker, for example, they can recognize atyrosine or a serine after phosphorylation, but not in the absence ofphosphate. In this way antibodies can be used to determine thephosphorylation state of a particular biomarker.

Antibodies are obtained commercially or produced using well-establishedmethods. To obtain antibodies specific for a single epitope of anantigen, antibody-secreting lymphocytes are isolated from the animal andimmortalized by fusing them with a cancer cell line. The fused cells arereferred to as hybridomas, and can continually grow and secrete antibodyin culture. Single hybridoma cells are isolated by dilution cloning togenerate cell clones that all produce the same antibody; theseantibodies can be referred to as monoclonal antibodies.

Polyclonal and monoclonal antibodies can be purified in several ways.For example, one can isolate an antibody using antigen-affinitychromatography which can be couple to bacterial proteins such as ProteinA, Protein G, Protein L or the recombinant fusion protein, Protein A/Gfollowed by detection of via UV light at 280 nm absorbance of the eluatefractions to determine which fractions contain the antibody. Protein A/Gcan bind to all subclasses of human IgG, making it useful for purifyingpolyclonal or monoclonal IgG antibodies whose subclasses have not beendetermined. In addition, Protein A/G can bind to IgA, IgE, IgM and (insome cases to a lesser extent) IgD. Protein A/G can bind to allsubclasses of mouse IgG but in some cases does not bind mouse IgA, IgMor serum albumin. This feature can allow Protein A/G to be used forpurification and detection of mouse monoclonal IgG antibodies, withoutinterference from IgA, IgM and serum albumin.

Antibodies are derived from different classes or isotypes of moleculessuch as, for example, IgA, IgA IgD, IgE, IgM and IgG. The IgA can bedesigned for secretion in the bodily fluids while others, like the IgMare designed to be expressed on the cell surface. The antibody can be anIgG antibody. In some cases, IgG comprises two subunits including two“heavy” chains and two “light” chains. These can be assembled in asymmetrical structure and each IgG can have two identical antigenrecognition domains. The antigen recognition domain can be a combinationof amino acids from both the heavy and light chains. The molecule can beroughly shaped like a “Y” and the arms/tips of the molecule comprise theantigen-recognizing regions or Fab (fragment, antigen binding) region,while the stem of Fc (Fragment, crystallizable) region is notnecessarily involved in recognition and can be fairly constant. Theconstant region can be identical in all antibodies of the same isotype,but can differ in antibodies of different isotypes.

It is also possible to use an antibody to detect a protein afterfractionation by western blotting. Western blotting is used in somecases for the detection and/or measurement of protein or polypeptidebiomarkers.

Some detection methods can employ flow cytometry. Flow cytometry can bea laser based, biophysical technology that can be used for biomarkerdetection, quantification (cell counting) and cell isolation. Thistechnology can be used in the diagnosis of health disorders, especiallyblood cancers. In general, flow cytometry can comprise suspending singlecells in a stream of fluid. A beam of light (usually laser light) of asingle wavelength can be directed onto the stream of liquid, and thescatter light caused by a passing cell can be detected by an electronicdetection apparatus. A flow cytometry methodology useful in one or moremethods described herein can include Fluorescence-activated cell sorting(FACS). FACS can use florescent-labeled antibodies to detect antigens oncell of interest. This additional feature of antibody labeling use inFACS can enable simultaneous multiparametric analysis and quantificationbased upon the specific light scattering and fluorescent characteristicsof each cell florescent-labeled cell and it provides physical separationof the population of cells of interest as well as traditional flowcytometry does.

A wide range of fluorophores can be used as labels in flow cytometry.Fluorophores can be typically attached to an antibody that recognizes atarget feature on or in the cell. Examples of suitable fluorescentlabels include, but are not limited to: fluorescein (FITC), 5,6-carboxymethyl fluorescein, Texas red, nitrobenz-2-oxa-1,3-diazol-4-yl(NBD), and the cyanine dyes Cy3, Cy3.5, Cy5, Cy5.5 and Cy7. OtherFluorescent labels such as Alexa Fluor® dyes, DNA content dye such asDAPI, and Hoechst dyes are well known in the art and can be easilyobtained from a variety of commercial sources. Each fluorophore can havea characteristic peak excitation and emission wavelength, and theemission spectra often overlap. The absorption and emission maxima,respectively, for these fluors can be: FITC (490 nm; 520 nm), Cy3 (554nm; 568 nm), Cy3.5 (581 nm; 588 nm), Cy5 (652 nm: 672 nm), Cy5.5 (682nm; 703 nm) and Cy7 (755 nm; 778 nm). The fluorescent labels can beobtained from a variety of commercial sources. Quantum dots can be usedin place of traditional fluorophores. Other methods that can be used fordetecting include isotope labeled antibodies, such as lanthanideisotopes.

Immunoassays optionally comprise immunohistochemistry.Immunohistochemistry is used to detect expression of the claimedbiomarkers in a tissue sample. The antibodies can be detected by directlabeling of the antibodies themselves, for example, with radioactivelabels, fluorescent labels, hapten labels such as, biotin, or an enzymesuch as horse radish peroxidase or alkaline phosphatase. Alternatively,unlabeled primary antibody can be used in conjunction with a labeledsecondary antibody, comprising antisera, polyclonal antisera or amonoclonal antibody specific for the primary antibody.Immunohistochemistry protocols are well known in the art and protocolsand antibodies are commercially available. Alternatively, one raises anantibody to the biomarkers or modified versions of the biomarker orbinding partners as disclosure herein that would be useful fordetermining the expression levels of the proteins in a tissue sample.

Some measurement of biomarkers comprises use of a biochip. Biochips canbe used to screen a large number of macromolecules. Biochips can bedesigned with immobilized nucleic acid molecules, full-length proteins,antibodies, affibodies (small molecules engineered to mimic monoclonalantibodies), aptamers (nucleic acid-based ligands) or chemicalcompounds. A chip could be designed to detect multiple macromoleculetypes on one chip. For example, a chip could be designed to detectnucleic acid molecules, proteins and metabolites on one chip. Thebiochip can be used to and designed to simultaneously analyze a panelbiomarker in a single sample, producing a subjects profile for thesebiomarkers. The use of the biochip allows for the multiple analyses tobe performed reducing the overall processing time and the amount ofsample required.

Protein microarray can be a particular type of biochip which can be usedwith the present disclosure. In some cases, the chip comprises a supportsurface such as a glass slide, nitrocellulose membrane, bead, ormicrotitre plate, to which an array of capture proteins can be bound inan arrayed format onto a solid surface. Protein array detection methodscan give a high signal and a low background. Detection probe molecules,typically labeled with a fluorescent dye, can be added to the array. Anyreaction between the probe and the immobilized protein can result inemission of a detectable signal. Such protein microarrays can be rapid,automated, and offer high sensitivity of protein biomarker read-outs fordiagnostic tests. However, it would be immediately appreciated to thoseskilled in the art that there are a variety of detection methods thatcan be used with this technology. Exemplary microarrays includeanalytical microarrays (also known as capture arrays), functionalprotein microarrays (also known as target protein arrays) and reversephase protein microarray (RPA).

Analytical protein microarrays can be constructed using a library ofantibodies, aptamers or affibodies. The array can be probed with acomplex protein solution such as a blood, serum or a cell lysate thatfunction by capturing protein molecules they specifically bind to.Analysis of the resulting binding reactions using various detectionsystems can provide information about expression levels of particularproteins in the sample as well as measurements of binding affinities andspecificities. This type of protein microarray can be especially usefulin comparing protein expression in different samples. Functional proteinmicroarrays can be constructed by immobilizing large numbers of purifiedfull-length functional proteins or protein domains and can be used toidentify protein-protein, protein-DNA, protein-RNA,protein-phospholipid, and protein-small molecule interactions, to assayenzymatic activity and to detect antibodies and demonstrate theirspecificity. These protein microarray biochips can be used to study thebiochemical activities of the entire proteome in a sample.

One or more biomarkers can be measured using reverse phase proteinmicroarray (RPA). Reverse phase protein microarray can be constructedfrom tissue and cell lysates that can be arrayed onto the microarray andprobed with antibodies against the target protein of interest. Theseantibodies can be detected with chemiluminescent, fluorescent orcolorimetric assays. In addition to the protein in the lysate, referencecontrol peptides can be printed on the slides to allow for proteinquantification. RPAs allow for the determination of the presence ofaltered proteins or other agents that may be the result of disease andpresent in a diseased cell.

One or more biomarkers can be measured using mass spectroscopy(alternatively referred to as mass spectrometry). Mass spectrometry (MS)can refer to an analytical technique that measures the mass-to-chargeratio of charged particles. It can be primarily used for determining theelemental composition of a sample or molecule, and for elucidating thechemical structures of molecules, such as peptides and other chemicalcompounds. MS works by ionizing chemical compounds to generate chargedmolecules or molecule fragments and measuring their mass-to-chargeratios MS instruments typically consist of three modules (1) an ionsource, which can convert gas phase sample molecules into ions (or, inthe case of electrospray ionization, move ions that exist in solutioninto the gas phase) (2) a mass analyzer, which sorts the ions by theirmasses by applying electromagnetic fields and (3) detector, whichmeasures the value of an indicator quantity and thus provides data forcalculating the abundances of each ion present.

Suitable mass spectrometry methods to be used with the presentdisclosure include but are not limited to, one or more of electrosprayionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS),matrix-assisted laser desorption ionization time-of-flight massspectrometry (MALDI-TOF-MS), surface-enhanced laserdesorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS),tandem liquid chromatography-mass spectrometry (LC-MS/MS) massspectrometry, desorption/ionization on silicon (DIOS), secondary ionmass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmosphericpressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS,APCI-(MS), atmospheric pressure photoionization mass spectrometry(APPI-MS), APPI-MS/MS, and APPI-(MS), quadrupole mass spectrometry,Fourier transform mass spectrometry (FTMS), and ion trap massspectrometry, where n can be an integer greater than zero.

LC-MS can be commonly used to resolve the components of a complexmixture. LC-MS method generally involves protease digestion anddenaturation (usually involving a protease, such as trypsin and adenaturant such as, urea to denature tertiary structure andiodoacetamide to cap cysteine residues) followed by LC-MS with peptidemass fingerprinting or LC-MS/MS (tandem MS) to derive sequence ofindividual peptides. LC-MS/MS can be used for proteomic analysis ofcomplex samples where peptide masses may overlap even with ahigh-resolution mass spectrometer. Samples of complex biological fluidslike human serum may be first separated on an SDS-PAGE gel or HPLC-SCXand then run in LC-MS/MS allowing for the identification of over 1000proteins.

While multiple mass spectrometric approaches are compatible with themethods of the disclosure as provided herein, in some applications it isdesired to quantify proteins in biological samples from a selectedsubset of proteins of interest. One such MS technique that is compatiblewith the present disclosure is Multiple Reaction Monitoring MassSpectrometry (MRM-MS), or alternatively referred to as Selected ReactionMonitoring Mass Spectrometry (SRM-MS).

The MRM-MS technique involves a triple quadrupole (QQQ) massspectrometer to select a positively charged ion from the peptide ofinterest, fragment the positively charged ion and then measure theabundance of a selected positively charged fragment ion. Thismeasurement is commonly referred to as a transition and/or transitionion. By way of illustrative example only, a peptide fragment comprisingthe amino acid sequence IAELLSPGSVDPLTR (SEQ ID NO: 27) can comprise oneor more of the following exemplary transition ion biomarkers provided inTable 5, below.

TABLE 5 Exemplary transition ions for the peptide sequenceIAELLSPGSVDPLTR (SEQ ID NO: 27) Transition Ion Amino Acid Sequence b1 Ib2 IA b3 IAE b4 IAEL (SEQ ID NO: 28) b5 IAELL (SEQ ID NO: 29) b6IAELLS (SEQ ID NO: 30) b7 IAELLSP (SEQ ID NO: 31) b8IAELLSPG (SEQ ID NO: 32) b9 IAELLSPGS (SEQ ID NO: 33) b10IAELLSPGSV (SEQ ID NO: 34) b11 IAELLSPGSVD (SEQ ID NO: 35) b12IAELLSPGSVDP (SEQ ID NO: 36) b13 IAELLSPGSVDPL (SEQ ID NO: 37) b14IAELLSPGSVDPLT (SEQ ID NO: 38) y14 AELLSPGSVDPLTR (SEQ ID NO: 39) y13ELLSPGSVDPLTR (SEQ ID NO: 40) y12 LLSPGSVDPLTR (SEQ ID NO: 41) y11LSPGSVDPLTR (SEQ ID NO: 42) y10 SPGSVDPLTR (SEQ ID NO: 43) Y9PGSVDPLTR (SEQ ID NO: 44) Y8 GSVDPLTR (SEQ ID NO: 45) y7SVDPLTR (SEQ ID NO: 46) y6 VDPLTR (SEQ ID NO: 47) Y5DPLTR(SEQ ID NO: 48) y4 PLTR(SEQ ID NO: 49) Y3 LTR y2 TR y1 R

In some applications the MRM-MS is coupled with High-Pressure LiquidChromatography (HPLC) and more recently Ultra High-Pressure LiquidChromatography (UHPLC). In other applications MRM-MS can be coupled withUHPLC with a QQQ mass spectrometer to make the desired LC-MS transitionmeasurements for all of the peptides and proteins of interest.

In some applications the utilization of a quadrupole time-of-flight(qTOF) mass spectrometer, time-of-flight time-of-flight (TOF-TOF) massspectrometer, Orbitrap mass spectrometer, quadrupole Orbitrap massspectrometer or any Quadrupolar Ion Trap mass spectrometer can be usedto select for a positively charged ion from one or more peptides ofinterest. The fragmented, positively charged ions can then be measuredto determine the abundance of a positively charged ion for thequantitation of the peptide or protein of interest.

In some applications the utilization of a time-of-flight (TOF),quadrupole time-of-flight (qTOF) mass spectrometer, time-of-flighttime-of-flight (TOF-TOF) mass spectrometer, Orbitrap mass spectrometeror quadrupole Orbitrap mass spectrometer is used to measure the mass andabundance of a positively charged peptide ion from the protein ofinterest without fragmentation for quantitation. In this application,the accuracy of the analyte mass measurement can be used as selectioncriteria of the assay. An isotopically labeled internal standard of aknown composition and concentration can be used as part of the massspectrometric quantitation methodology.

In some applications, time-of-flight (TOF), quadrupole time-of-flight(qTOF) mass spectrometer, time-of-flight time-of-flight (TOF-TOF) massspectrometer, Orbitrap mass spectrometer or quadrupole Orbitrap massspectrometer is used to measure the mass and abundance of a protein ofinterest for quantitation. In this application, the accuracy of theanalyte mass measurement can be used as selection criteria of the assay.Optionally this application can use proteolytic digestion of the proteinprior to analysis by mass spectrometry. An isotopically labeled internalstandard of a known composition and concentration can be used as part ofthe mass spectrometric quantitation methodology.

In some applications, various ionization techniques can be coupled tothe mass spectrometers provide herein to generate the desiredinformation. Non-limiting exemplary ionization techniques that are usedwith the present disclosure include but are not limited to MatrixAssisted Laser Desorption Ionization (MALDI), Desorption ElectrosprayIonization (DESI), Direct Assisted Real Time (DART), Surface AssistedLaser Desorption Ionization (SALDI), or Electrospray Ionization (ESI).

In some applications, HPLC and UHPLC can be coupled to a massspectrometer a number of other peptide and protein separation techniquescan be performed prior to mass spectrometric analysis. Some exemplaryseparation techniques which can be used for separation of the desiredanalyte (for example, peptide or protein) from the matrix backgroundinclude but are not limited to Reverse Phase Liquid Chromatography(RP-LC) of proteins or peptides, offline Liquid Chromatography (LC)prior to MALDI, 1 dimensional gel separation, 2-dimensional gelseparation, Strong Cation Exchange (SCX) chromatography, Strong AnionExchange (SAX) chromatography, Weak Cation Exchange (WCX), and WeakAnion Exchange (WAX). One or more of the above techniques can be usedprior to mass spectrometric analysis.

One or more biomarkers can be measured using a microarray. Differentialgene expression can also be identified, or confirmed using themicroarray technique. Thus, the expression profile biomarkers can bemeasured in either fresh or fixed tissue, using microarray technology.In this method, polynucleotide sequences of interest (including cDNAsand oligonucleotides) can be plated, or arrayed, on a microchipsubstrate. The arrayed sequences can be then hybridized with specificDNA probes from cells or tissues of interest. The source of mRNA can betotal RNA isolated from a biological sample, and corresponding normaltissues or cell lines may be used to determine differential expression.

One or more biomarkers can be measured by sequencing. Differential geneexpression can also be identified, or confirmed using the sequencingtechnique. Thus, the expression profile biomarkers can be measured ineither fresh or fixed sample, using sequencing technology. In thismethod, polynucleotide sequences of interest (including cDNAs andoligonucleotides) can used as templates to synthesize sequencinglibraries. The libraries can be sequenced, and the reads mapped to anappropriate reference. The source of mRNA can be total RNA isolated froma biological sample, and corresponding normal tissues or cell lines maybe used to determine differential expression. Exemplary sequencingtechniques can include, for example emulsion PCR (pyrosequencing fromRoche 454, semiconductor sequencing from Ion Torrent, SOLiD sequencingby ligation from Life Technologies, sequencing by synthesis fromIntelligent Biosystems), bridge amplification on a flow cell (e.g.Solexa/Illumina), isothermal amplification by Wildfire technology (LifeTechnologies) or rolonies/nanoballs generated by rolling circleamplification (Complete Genomics, Intelligent Biosystems, Polonator).Sequencing technologies like Heliscope (Helicos), SMRT technology(Pacific Biosciences) or nanopore sequencing (Oxford Nanopore) allowdirect sequencing of single molecules without prior clonal amplificationmay be suitable sequencing platforms. Sequencing may be performed withor without target enrichment. In some cases, polynucleotides from asample are amplified by any suitable means prior to and/or duringsequencing.

PCR amplified inserts of cDNA clones can be applied to a substrate in adense array. Preferably at least 10,000 nucleotide sequences can beapplied to the substrate. The microarrayed genes, immobilized on themicrochip at 10,000 elements each, can be suitable for hybridizationunder stringent conditions. Fluorescently labeled cDNA probes may begenerated through incorporation of fluorescent nucleotides by reversetranscription of RNA extracted from tissues of interest. Labeled cDNAprobes applied to the chip hybridize with specificity to each spot ofDNA on the array. After stringent washing to remove non-specificallybound probes, the microarray chip can be scanned by a device such as,confocal laser microscopy or by another detection method, such as a CCDcamera. Quantitation of hybridization of each arrayed element allows forassessment of corresponding mRNA abundance. With dual colorfluorescence, separately labeled cDNA probes generated from two sourcesof RNA can be hybridized pair-wise to the array. The relative abundanceof the transcripts from the two sources corresponding to each specifiedgene can be thus determined simultaneously. Microarray analysis can beperformed by commercially available equipment, following manufacturer'sprotocols.

One or more biomarkers can be measured using qRT-PCR, which can be usedto compare mRNA levels in different sample populations, in normal andtumor tissues, with or without drug treatment, to characterize patternsof gene expression, to discriminate between closely related mRNAs, andto analyze RNA structure. The first step in gene expression profiling byRT-PCR can be extracting RNA from a biological sample followed by thereverse transcription of the RNA template into cDNA and amplification bya PCR reaction. The reverse transcription reaction step can be generallyprimed using specific primers, random hexamers, or oligo-dT primers,depending on the goal of expression profiling. Reverse transcriptasescan be avilo myeloblastosis virus reverse transcriptase (AMV-RT) and/orMoloney murine leukemia virus reverse transcriptase (MLV-RT).

Although the PCR step can use a variety of thermostable DNA-dependentDNA polymerases, it typically employs the Taq DNA polymerase, which canhave a 5′-3′ nuclease activity but lacks a 3′-5′ proofreadingendonuclease activity. Thus, TaqMan™ PCR typically utilizes the5′-nuclease activity of Taq or Tth polymerase to hydrolyze ahybridization probe bound to its target amplicon, but any enzyme withequivalent 5′ nuclease activity can be used. Two oligonucleotide primerscan be used to generate an amplicon typical of a PCR reaction. A thirdoligonucleotide, or probe, can be designed to detect nucleotide sequencelocated between the two PCR primers. The probe can be non-extendible byTaq DNA polymerase enzyme, and can be labeled with a reporterfluorescent dye and a quencher fluorescent dye. Any laser-inducedemission from the reporter dye can be quenched by the quenching dye whenthe two dyes are located close together as they are on the probe. Duringthe amplification reaction, the Taq DNA polymerase enzyme can cleave theprobe in a template-dependent manner. The resultant probe fragments candisassociate in solution, and signal from the released reporter dye canbe freed from the quenching effect of the second fluorophore. Onemolecule of reporter dye can be liberated for each new moleculesynthesized, and detection of the unquenched reporter dye can providebasis for quantitative interpretation of the data.

TaqMan™ RT-PCR can be performed using commercially available equipment,such as, for example, ABI PRISM 7700™ Sequence Detection System™(Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), orLightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In apreferred embodiment, the 5′ nuclease procedure is run on a real-timequantitative PCR device such as the ABI PRISM 7700™ Sequence DetectionSystem™. The system comprises a thermocycler, laser, charge-coupleddevice (CCD), camera and computer. The system includes software forrunning the instrument and for analyzing the data. 5′-Nuclease assaydata are initially expressed as Ct, or the threshold cycle. As discussedabove, fluorescence values are recorded during every cycle and representthe amount of product amplified to that point in the amplificationreaction. The point when the fluorescent signal is first recorded asstatistically significant can be the threshold cycle (Ct).

To minimize errors and the effect of sample-to-sample variation, RT-PCRcan be performed using an internal standard. An internal standard can beexpressed at a constant level among different tissues, and can beunaffected by the experimental treatment. RNAs most frequently used tonormalize patterns of gene expression are mRNAs for the housekeepinggenes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and Beta-Actin.

A more recent variation of the RT-PCR technique can include the realtime quantitative PCR, which can measure PCR product accumulationthrough a dual-labeled fluorogenic probe (i.e., TaqMan™ probe). Realtime PCR can be compatible both with quantitative competitive PCR, whereinternal competitor for each target sequence can be used fornormalization, and with quantitative comparative PCR using anormalization gene contained within the sample, or a housekeeping genefor RT-PCR. For further details see, for example Held et al., GenomeResearch 6:986-994 (1996).

Normalization of Data

Measurement data used in the methods, systems, kits and compositionsdisclosed herein are optionally normalized. Normalization refers to aprocess to correct for example, differences in the amount of genes orprotein levels assayed and variability in the quality of the templateused, to remove unwanted sources of systematic variation measurementsinvolved in the processing and detection of genes or protein expression.Other sources of systematic variation are attributable to laboratoryprocessing conditions.

In some instances, normalization methods are used for the normalizationof laboratory processing conditions. Non-limiting examples ofnormalization of laboratory processing that may be used with methods ofthe disclosure include but are not limited to: accounting for systematicdifferences between the instruments, reagents, and equipment used duringthe data generation process, and/or the date and time or lapse of timein the data collection.

Assays can provide for normalization by incorporating the expression ofcertain normalizing standard genes or proteins, which do notsignificantly differ in expression levels under the relevant conditions,that is to say they are known to have a stabilized and consistentexpression level in that particular sample type. Suitable normalizationgenes and proteins that can be used with the present disclosure includehousekeeping genes. (See, for example, E. Eisenberg, et al., Trends inGenetics 19(7):362-365 (2003). In some applications, the normalizingbiomarkers (genes and proteins), also referred to as reference genes,known not to exhibit meaningfully different expression levels insubjects with advanced colorectal adenoma or CRC as compared to controlsubjects without advanced colorectal adenoma or CRC. In someapplications, it may be useful to add a stable isotope labeled standardswhich can be used and represent an entity with known properties for usein data normalization. In other applications, a standard, fixed samplecan be measured with each analytical batch to account for instrument andday-to-day measurement variability.

Clinical Outcome Score

Machine learning algorithms for sub-selecting discriminating biomarkersand optionally subject characteristics, and for building classificationmodels, are used in some methods and systems herein to determineclinical outcome scores. These algorithms include, but are not limitedto, elastic networks, random forests, support vector machines, andlogistic regression. These algorithms can aid in selection of importantbiomarker features and transform the underlying measurements into ascore or probability relating to, for example, clinical outcome, diseaserisk, disease likelihood, presence or absence of disease, treatmentresponse, and/or classification of disease status.

A clinical outcome score is determined by comparing a level of at leasttwo biomarkers in the biological sample obtained from the subject to areference level of the at least two biomarkers. Alternately or incombination, a clinical outcome score is determined by comparing asubject-specific profile of a biomarker panel to a reference profile ofthe biomarker panel. In some cases, a reference level or referenceprofile represents a known diagnosis. For example, a reference level orreference profile represents a positive diagnosis of advanced colorectaladenoma. A reference level or reference profile can represent a positivediagnosis of CRC. As another example, a reference level or referenceprofile represents a negative diagnosis of advanced colorectal adenoma.Similarly, a reference level or reference profile can represent anegative diagnosis of CRC

In some cases, an increase in a score indicates an increased likelihoodof one or more of: a poor clinical outcome, good clinical outcome, highrisk of disease, low risk of disease, complete response, partialresponse, stable disease, non-response, and recommended treatments fordisease management. In some cases, a decrease in the quantitative scoreindicates an increased likelihood of one or more of: a poor clinicaloutcome, good clinical outcome, high risk of disease, low risk ofdisease, complete response, partial response, stable disease,non-response, and recommended treatments for disease management.

A similar biomarker profile from a patient to a reference profile oftenindicates an increased likelihood of one or more of: a poor clinicaloutcome, good clinical outcome, high risk of disease, low risk ofdisease, complete response, partial response, stable disease,non-response, and recommended treatments for disease management. In someapplications, a dissimilar biomarker profile from a patient to areference profile indicates one or more of: an increased likelihood of apoor clinical outcome, good clinical outcome, high risk of disease, lowrisk of disease, complete response, partial response, stable disease,non-response, and recommended treatments for disease management.

An increase in one or more biomarker threshold values often indicates anincreased likelihood of one or more of: a poor clinical outcome, goodclinical outcome, high risk of disease, low risk of disease, completeresponse, partial response, stable disease, non-response, andrecommended treatments for disease management. In some applications, adecrease in one or more biomarker threshold values indicates anincreased likelihood of one or more of: a poor clinical outcome, goodclinical outcome, high risk of disease, low risk of disease, completeresponse, partial response, stable disease, non-response, andrecommended treatments for disease management.

An increase in at least one of a quantitative score, one or morebiomarker thresholds, a similar biomarker profile values indicates anincreased likelihood of one or more of: a poor clinical outcome, goodclinical outcome, high risk of disease, low risk of disease, completeresponse, partial response, stable disease, non-response, andrecommended treatments for disease management. Similarly, a decrease inat least one of a quantitative score, one or more biomarker thresholds,a similar biomarker profile values or combinations thereof indicates anincreased likelihood of one or more of: a poor clinical outcome, goodclinical outcome, high risk of disease, low risk of disease, completeresponse, partial response, stable disease, non-response, andrecommended treatments for disease management.

A clinical outcome score is optionally updated based on additionalinformation derived during treatment. Such updates often comprise theaddition of other biomarkers. Such biomarkers include additionalproteins, metabolite accumulation levels, physical characteristics ofthe subject (e.g., age, race, weight, demographic history), medicalhistory of the subject (e.g., family history of advanced colorectaladenoma, prior quantitative score of the protein panels). Such updatescan comprise an adjustment of the test sensitivity. Such updates cancomprise an adjustment of the test sensitivity. Such updates cancomprise an adjustment of the test thresholds. Such updates can comprisean adjustment of the predicted clinical outcomes.

For example, in some cases a patient at risk of advanced colorectaladenoma is tested using a panel as disclosed herein. The patient may becategorized as having or being likely to have, advanced colorectaladenoma. In some cases, the thresholds of a protein panel disclosedherein will be updated based on additional biomarkers, such as age ofthe patient. For example, a patient over the age of 60 is more likelythan a patient under 60 to have advanced colorectal adenoma. Therefore,the positive predictive value of the protein panel can be higher in thepopulation over 60 than the population under 60. In some cases, thethreshold for proteins in the protein panel can be altered based on anadditional biomarker (e.g., age) to reflect this, such as by loweringthe threshold in a population over 60 compared to a population under 60.A patient's personal threshold may be updated based on previous testresults. For example, a patient may have an indeterminate or positiveclinical outcome score. Such a patient may have additional testsrecommended. Such a patient may have a colonoscopy recommended. Suchadditional tests and colonoscopies can come back negative, and thepersistence of an indeterminate or positive clinical outcome score canlead to the patient's thresholds being updated to reflect theirpersistent indeterminate or positive clinical outcome score.

In some cases, the specificity and sensitivity of the test is adjustedbased on an additional biomarker. For example, the protein panelsdisclosed herein may have different sensitivities or specificities inpopulations of individuals with a given genetic or racial background. Insome cases, based on an additional biomarker, the clinical outcome scoremay be adjusted to reflect a changing sensitivity or specificity of thetest.

Treatment and Diagnostic Regimens

Provided herein are treatment and diagnostic regimens for implementingany of the methods described herein for detecting a presence or absenceof advanced colorectal adenoma and treatment of the same.

Provided herein are methods for detecting a presence or absence ofcolorectal cancer. Methods disclosed herein can comprise performing atest for colorectal cancer, performing a colonoscopy, during whichdetected colorectal cancers are surgically excised or otherwise removed,and performing the test for colorectal cancer a second time at a laterdate. The second test can be positive and a second colonoscopy can beperformed. In some cases, the second colonoscopy can include searchingfor and monitoring sessile colorectal cancers. In some cases, the secondcolonoscopy can include searching for and surgically removing sessilecolorectal cancers. In some cases the second test for colorectal cancercan be positive and an additional treatment regimen can be recommended.In some cases, the second test for colorectal cancer can be negative andno additional testing can be recommended. In some cases, the second testfor advanced colorectal adenoma can be negative and more frequenttesting can be recommended for a given period of time.

In some cases, a positive clinical outcome score can lead to therecommendation of a drug therapeutic regimen. For example, a positiveclinical outcome score can result in the recommendation that a Wntpathway inhibitor be administered to the subject. After the Wnt pathwayinhibitor is administered, a second test for advanced colorectal adenomacan be administered to the subject. A negative or less severe clinicaloutcome score can indicate that the treatment is effective. A secondpositive or more severe clinical outcome score can indicate that thetreatment is not effective.

Computer Systems

Provided herein are computer systems for implementing any of the methodsdescribed herein for detecting a presence or absence of at least one ofadvanced colorectal adenoma and CRC. Also provided herein are computersystems for detecting a presence or absence of CRC. Computer systemsdisclosed herein comprises a memory unit. The memory unit can beconfigured to receive data comprising measurement of a biomarker panelfrom a biological sample of a subject. The biomarker panel can be anybiomarker panel described herein. For example, the biomarker panel cancomprise at least two biomarkers selected from the group comprisingAACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR, and in some casesincludes age as an additional biomarker. Optionally, the biomarker panelincludes CATD, CLUS, GDF15, and SAA1, and in some cases includes age asan additional biomarker.

Computer systems disclosed herein comprise computer executable code forperforming at least one of: generating a subject-specific profile of abiomarker panel described herein based upon the measurement data,comparing the subject-specific profile of the biomarker panel to areference profile of the biomarker panel, and determining a likelihoodof advanced colorectal adenoma in the subject. Computer systemsdisclosed herein comprises computer executable code for performing atleast one of: generating a subject-specific profile of a biomarker paneldescribed herein based upon the measurement data, comparing thesubject-specific profile of the biomarker panel to a reference profileof the biomarker panel, and determining a likelihood of CRC in thesubject.

Additionally, provided herein are computer systems for implementing anyof the methods described herein for detecting a presence or absence ofat least one of advanced colorectal adenoma and CRC. For example,provided herein are computer systems for detecting a presence or absenceof advanced colorectal adenoma. Also provided herein are computersystems for detecting a presence or absence of CRC. Computer systemsdisclosed herein comprises a memory unit. The memory unit can beconfigured to receive data comprising measurement of a biomarker panelfrom a biological sample of a subject. The biomarker panel can be anybiomarker panel described herein. For example, the biomarker panel cancomprise at least two biomarkers selected from the group comprisingAACT, CATD, CEA, CO3, CO9, MIF, PSGL, SEPR, CATD, CLUS, GDF15, and SAA1.

Computer systems disclosed herein optionally comprise computerexecutable code for performing at least one of: generating asubject-specific profile of a biomarker panel described herein basedupon the measurement data, comparing the subject-specific profile of thebiomarker panel to a reference profile of the biomarker panel, anddetermining a likelihood of advanced colorectal adenoma in the subject.Computer systems disclosed herein optionally comprise computerexecutable code for performing at least one of: generating asubject-specific profile of a biomarker panel described herein basedupon the measurement data, comparing the subject-specific profile of thebiomarker panel to a reference profile of the biomarker panel, anddetermining a likelihood of CRC in the subject.

Computer systems described herein optionally comprisecomputer-executable code for performing any of the algorithms describedherein. The computer system can further comprise computer-executablecode for providing a report communicating the presence or absence of theat least one of advanced colorectal adenoma and CRC, for recommending acolonoscopy, sigmoidoscopy, or colorectal tissue biopsy, and/or forrecommending a treatment. In some embodiments, the computer systemexecutes instructions contained in a computer-readable medium.

In some embodiments, the processor is associated with one or morecontrollers, calculation units, and/or other units of a computer system,or implanted in firmware. In some embodiments, one or more steps of themethod are implemented in hardware. In some embodiments, one or moresteps of the method are implemented in software. Software routines maybe stored in any computer readable memory unit such as flash memory,RAM, ROM, magnetic disk, laser disk, or other storage medium asdescribed herein or known in the art. Software may be communicated to acomputing device by any known communication method including, forexample, over a communication channel such as a telephone line, theinternet, a wireless connection, or by a transportable medium, such as acomputer readable disk, flash drive, etc. The one or more steps of themethods described herein may be implemented as various operations,tools, blocks, modules and techniques which, in turn, may be implementedin firmware, hardware, software, or any combination of firmware,hardware, and software. When implemented in hardware, some or all of theblocks, operations, techniques, etc. may be implemented in, for example,an application specific integrated circuit (ASIC), custom integratedcircuit (IC), field programmable logic array (FPGA), or programmablelogic array (PLA).

FIG. 19 depicts an exemplary computer system 1900 adapted to implement amethod described herein. The system 1900 includes a central computerserver 1901 that is programmed to implement exemplary methods describedherein. The server 1901 includes a central processing unit (CPU, also“processor”) 1905 which can be a single core processor, a multi coreprocessor, or plurality of processors for parallel processing. Theserver 1901 also includes memory 1910 (for example random access memory,read-only memory, flash memory); electronic storage unit 1915 (forexample hard disk); communications interface 1920 (for example networkadaptor) for communicating with one or more other systems; andperipheral devices 1925 which may include cache, other memory, datastorage, and/or electronic display adaptors. The memory 1910, storageunit 1915, interface 1920, and peripheral devices 1925 are incommunication with the processor 1905 through a communications bus(solid lines), such as a motherboard. The storage unit 1915 can be adata storage unit for storing data. The server 1901 is operativelycoupled to a computer network (“network”) 1930 with the aid of thecommunications interface 1920. The network 1930 can be the Internet, anintranet and/or an extranet, an intranet and/or extranet that is incommunication with the Internet, a telecommunication or data network.The network 1930 in some cases, with the aid of the server 1901, canimplement a peer-to-peer network, which may enable devices coupled tothe server 1901 to behave as a client or a server.

The storage unit 1915 can store files, such as subject reports, and/orcommunications with the caregiver, sequencing data, data aboutindividuals, or any aspect of data associated with the invention.

The server can communicate with one or more remote computer systemsthrough the network 1930. The one or more remote computer systems maybe, for example, personal computers, laptops, tablets, telephones, Smartphones, or personal digital assistants.

In some situations the system 1900 includes a single server 1901. Inother situations, the system includes multiple servers in communicationwith one another through an intranet, extranet and/or the Internet.

The server 1901 can be adapted to store measurement data, patientinformation from the subject, such as, for example, polymorphisms,mutations, medical history, family history, demographic data and/orother information of potential relevance. Such information can be storedon the storage unit 1915 or the server 1901 and such data can betransmitted through a network.

Methods as described herein are in some cases implemented by way ofmachine (or computer processor) executable code (or software) stored onan electronic storage location of the server 1901, such as, for example,on the memory 1910, or electronic storage unit 1915. During use, thecode can be executed by the processor 1905. In some cases, the code canbe retrieved from the storage unit 1915 and stored on the memory 1910for ready access by the processor 1905. In some situations, theelectronic storage unit 115 can be precluded, and machine-executableinstructions are stored on memory 1910. Alternatively, the code can beexecuted on a second computer system 1940.

Aspects of the systems and methods provided herein, such as the server1901, can be embodied in programming. Various aspects of the technologymay be thought of as “products” or “articles of manufacture” typicallyin the form of machine (or processor) executable code and/or associateddata that is carried on or embodied in a type of machine readablemedium. Machine-executable code can be stored on an electronic storageunit, such memory (for example, read-only memory, random-access memory,flash memory) or a hard disk. “Storage” type media can include any orall of the tangible memory of the computers, processors or the like, orassociated modules thereof, such as various semiconductor memories, tapedrives, disk drives and the like, which may provide non-transitorystorage at any time for the software programming. All or portions of thesoftware may at times be communicated through the Internet or variousother telecommunication networks. Such communications, for example, mayenable loading of the software from one computer or processor intoanother, for example, from a management server or host computer into thecomputer platform of an application server. Thus, another type of mediathat may bear the software elements includes optical, electrical, andelectromagnetic waves, such as used across physical interfaces betweenlocal devices, through wired and optical landline networks and overvarious air-links. The physical elements that carry such waves, such aswired or wireless likes, optical links, or the like, also may beconsidered as media bearing the software. As used herein, unlessrestricted to non-transitory, tangible “storage” media, terms such ascomputer or machine “readable medium” can refer to any medium thatparticipates in providing instructions to a processor for execution.

Hence, a machine readable medium, such as computer-executable code, maytake many forms, including but not limited to, tangible storage medium,a carrier wave medium, or physical transmission medium. Non-volatilestorage media can include, for example, optical or magnetic disks, suchas any of the storage devices in any computer(s) or the like, such maybe used to implement the system. Tangible transmission media caninclude: coaxial cables, copper wires, and fiber optics (including thewires that comprise a bus within a computer system). Carrier-wavetransmission media may take the form of electric or electromagneticsignals, or acoustic or light waves such as those generated during radiofrequency (RF) and infrared (IR) data communications. Common forms ofcomputer-readable media therefore include, for example: a floppy disk, aflexible disk, hard disk, magnetic tape, any other magnetic medium, aCD-ROM, DVD, DVD-ROM, any other optical medium, punch cards, paper tame,any other physical storage medium with patterns of holes, a RAM, a ROM,a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, acarrier wave transporting data or instructions, cables, or linkstransporting such carrier wave, or any other medium from which acomputer may read programming code and/or data. Many of these forms ofcomputer readable media may be involved in carrying one or moresequences of one or more instructions to a processor for execution.

The results of detection of a presence or absence of at least one of anadvanced colorectal adenoma and CRC, generating a subject report, and/orcommunicating the report to a caregiver can be presented to a user withthe aid of a user interface, such as a graphical user interface.

A computer system may be used to implement one or more steps of a methoddescribed herein, including, for example, sample collection, sampleprocessing, measurement of an amount of one or more proteins describedherein to produce measurement data, determination of a ratio of aprotein to another protein to produce measurement data, comparingmeasurement data to a reference amount, generating a subject-specificprofile of a biomarker panel, comparing the subject-specific profile toa reference profile, receiving medical history, receiving medicalrecords, receiving and storing measurement data obtained by one or moremethods described herein, analyzing said measurement data to determine apresence or absence of at least one of an advanced colorectal adenomaand CRC (for example, by performing an algorithm described herein),generating a report, and reporting results to a receiver.

A client-server and/or relational database architecture can be used inany of the methods described herein. In general, a client-serverarchitecture is a network architecture in which each computer or processon the network is either a client or a server. Server computers can bepowerful computers dedicated to managing disk drives (file servers),printers (print servers), or network traffic (network servers). Clientcomputers can include PCs (personal computers) or workstations on whichusers run applications, as well as example output devices as disclosedherein. Client computers can rely on server computers for resources,such as files, devices, and even processing power. The server computerhandles all of the database functionality. The client computer can havesoftware that handles front-end data management and receive data inputfrom users.

After performing a calculation, a processor can provide the output, suchas from a calculation, back to, for example, the input device or storageunit, to another storage unit of the same or different computer system,or to an output device. Output from the processor can be displayed by adata display, for example, a display screen (for example, a monitor or ascreen on a digital device), a print-out, a data signal (for example, apacket), a graphical user interface (for example, a webpage), an alarm(for example, a flashing light or a sound), or a combination of any ofthe above. In an embodiment, an output is transmitted over a network(for example, a wireless network) to an output device. The output devicecan be used by a user to receive the output from the data-processingcomputer system. After an output has been received by a user, the usercan determine a course of action, or can carry out a course of action,such as a medical treatment when the user is medical personnel. In someembodiments, an output device is the same device as the input device.Example output devices include, but are not limited to, a telephone, awireless telephone, a mobile phone, a PDA, a flash memory drive, a lightsource, a sound generator, a fax machine, a computer, a computermonitor, a printer, an iPod, and a webpage. The user station may be incommunication with a printer or a display monitor to output theinformation processed by the server. Such displays, output devices, anduser stations can be used to provide an alert to the subject or to acaregiver thereof.

Data relating to the present disclosure can be transmitted over anetwork or connections for reception and/or review by a receiver. Thereceiver can be but is not limited to the subject to whom the reportpertains; or to a caregiver thereof, for example, a health careprovider, manager, other healthcare professional, or other caretaker; aperson or entity that performed and/or ordered the genotyping analysis;a genetic counselor. The receiver can also be a local or remote systemfor storing such reports (for example servers or other systems of a“cloud computing” architecture). In one embodiment, a computer-readablemedium includes a medium suitable for transmission of a result of ananalysis of a biological sample.

Kits

The present disclosure also provides kits. In some cases, a kitdescribed herein comprises one or more compositions, reagents, and/ordevice components for measuring and/or detecting one or more biomarkersdescribed herein. A kit as described herein can further compriseinstructions for practicing any of the methods provided herein. The kitscan further comprise reagents to enable the detection of biomarker byvarious assays types such as ELISA assay, immunoassay, protein chip ormicroarray, mass spectrometry, immunohistochemistry, flow cytometry, orhigh content cell screening. Kits can also comprise a computer readablemedium comprising computer executable code for implementing a methoddescribed herein.

In some embodiments, a kit provided herein comprises antibodies to thebiomarkers described elsewhere in the disclosure. A kit may comprise atleast two antibodies that are each reactive against a biomarkersselected from the group consisting of CATD, CLUS, GDF15, SAA1, AACT,CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. In some cases, a kit providedherein comprises antibodies to AACT, CATD, CEA, CO3, CO9, MIF, PSGL, andSEPR. In other cases, a kit provided herein comprises antibodies toCATD, CLUS, GDF15, and SAA1.

In some embodiments, kits described herein include a packaging material.As used herein, the term “packaging material” can refer to a physicalstructure housing the components of the kit. The packaging material canmaintain sterility of the kit components, and can be made of materialcommonly used for such purposes (for example, paper, corrugated fiber,glass, plastic, foil, ampules, etc.). Kits can also include a bufferingagent, a preservative, or a protein/nucleic acid stabilizing agent. Kitscan include components for obtaining a biological sample from a patient.Non-limiting examples of such components can be gloves, hypodermicneedles or syringes, tubing, tubes or vessels to hold the biologicalsample, sterilization components (e.g. isopropyl alcohol wipes orsterile gauze), and/or cooling material (e.g., freezer pack, dry ice, orice).

In some cases, kits disclosed herein are used in accordance of any ofthe disclosed methods.

Panel Development Study Design and Patient Sample Collection

300 total samples were selected for analysis, taken from the EndoscopyII study performed at the Hvidovre Hospital in Denmark. In this study,45-mL blood samples were collected from enrolled participants acrossseven different centers prior to performing a colonoscopy. Blood sampleswere stored at −80° C. with constant monitoring. Co-morbidities wererecorded by ICD codes, and pathology, disease, and death reports wereretained on file. Participants entered the study based upon observedsymptoms, such as pain, bleeding, and anemia, which suggested furthermedical follow-up. Participants had no prior history of malignancy.Participants had no previous bowel neoplasia. Participants were notmembers of FAP or HNPCC families. Participants had not had a majoroperation in the preceding three months. Participants had not undergonea prior bowel endoscopy.

TABLE 5 Characteristics of patients enrolled in study Group Count CRC512 Colon/Rectal 320/192 Other primary 177 malignancies Adenoma 699High/Low risk 198/501 Colonic/Rectal 498/201 Benign bowel lesions 1,176Negative findings, with co- 1,014 morbidity Negative findings, without1,113 co-morbidity Total 4,698

Enrolled patients received colonoscopies to diagnosis any problemsassociated with the colon and rectum, and the results were used toconfirm the presence or absence of colorectal cancers and/or polyps. Forthe biomarker discovery study performed here, the 302 blood plasmasamples selected for analysis comprised 150 control samples that had nocomorbidities and no adverse findings from colonoscopy, and 150 diseasesamples that had confirmed colorectal cancer or advanced adenoma lesionsin advanced stage. For this study, advanced colorectal adenomas weredefined as having at least one of the following: any adenoma >=1 cm,sessile serrated polyps >=1 cm, adenomas with high grade dysplasia, oradenomas with villous histological features. The control and diseasesamples were matched in pairs for age, gender, and enrollment site (seeFIGS. 17A-17B). The 300 samples were further divided into discovery andvalidation sets, each with 75 control samples and 75 disease samples. Tomore rigorously test the generalization performance of the investigatedbiomarker panels in the validation set, the discovery and validationsets consisted of patent samples from non-overlapping sites. A summarytable of the samples and their characteristic is provided in Table 7.

Data Preparation

A total of 300 samples were analyzed using ELISA for 30 differentproteins, resulting in a concentration measurement (e.g. accumulationlevel) for each of the 30 proteins across the 302 samples. 300 totalsamples were analyzed using ELISA, targeted proteomics (TP) and SATplatforms quantifying protein levels for 226 total proteins (ELISA: 30,SAT: 9, TP: 187). An additional mode of data collection was used,comprising unlabeled liquid chromatography/mass spectrometry (LCMS)measurements. For the LCMS data collection, the protein identity of themeasured signals is not known a priori so the resulting measurements aretreated as anonymous marker values, simply referred to with arbitrary IDnumbers and their m/z and LC time locations. Data from these four assayplatforms were analyzed both individually and in combination with oneanother to find the top performing biomarker panels within the discoveryset. Unlabeled LCMS features present in the marker panel includedC3218600, having an m/z of 1465.78, an LC time of 14.3 minutes and acharge state 1; C387796, having an m/z 1051.55, an LC of 3.1 minutes,and a charge state 1; C597612, having an m/z of 845.44 and an LC of 2.8minutes and a charge state of 1; C979276, having m/z 752.91, 20.6minutes and a charge state of 2.

After data collection, the concentration values were prepared in avariety of ways. For some analyses, the concentration measurements werelog 2 transformed, while for others, the concentration values were leftuntransformed. Analyses were also performed on measurements that wereboth standardized (zero mean, unit variance) and un-standardized (i.e.original measurements). For some analyses, age interaction terms wereadded to the standard marker concentration values. Here, the product ofall age and marker pairs were calculated and added into the total set ofmarkers for analysis. In other analyses, the ratios of marker pairs werecalculated and used as new marker values for classification builds.

Classification Analysis

The goal of the classification analysis was to determine the topperforming marker panels and classification models that distinguishbetween samples with and without colorectal cancer. Classifier modelsand the associated classification performance were assessed using a 10by 10-fold cross validation procedure. The 10 by 10-fold crossvalidation was performed using the discovery set only, and incorporatedfeature selection and classification model assembly. In the crossvalidation procedure, feature selection was first applied to reduce thenumber of features used, followed by development of the classifier modeland subsequent classification performance evaluation. For each of the10-fold cross validations, the data were segregated into 10 splits eachcontaining 90% of the samples as a training set and the remaining 10% ofthe samples as a testing set. In this process, each sample was evaluatedone time in a test set. The feature selection and model assembly wasperformed using the training set only, and these models were thenapplied to the testing set to evaluate classifier performance, typicallyvia the area under the curve (AUC) from the receiver operatingcharacteristic (ROC) plot. Here, the mean or median AUC value obtainedfrom each of the 10 10-fold cross validation procedures was used toassess the overall marker panel and classification model performance.

To investigate the performance of different sized marker panels, avariety of feature selection and reduction methods were used includingElastic Network feature selection, Random Forest feature importanceranking, t-test p-value ranking, hierarchical clustering, and exhaustivecombination search. With the exception of exhaustive combination search,the feature selection methods were embedded within the individual foldsof the cross validation procedure to incorporate the variability ofmarker selection into the final performance assessment for a givenclassifier model build. For the exhaustive combination search, alln-choose-r combinations of features were evaluated, where a particularcombination was selected prior to model building and used across all thecross validation folds. For both computational feasibility reasons andto limit the possibility for over-fitting, n and r were chosen to haverelatively small values, with n typically <=30 total markers, and rtypically between 2 and 10.

Within the 10 by 10-fold cross validation folds and after the featureselection step, a classifier model was built using one of severalclassification algorithms including, as examples, the support vectormachine (SVM) algorithm, the Random Forest algorithm, Elastic Network(ENet) regression models with and without boosting, k-nearest neighbors(kNN), and combinations of these models applied in an ensemble. Theclassification models were built using established classificationmodeling packages implemented in the R statistical programming language.In the case of the ensemble models, individual classification modelswere built using two or more of the described algorithms, and theresulting classification scores were combined in a linear combination toobtain a final classification score. Another classification modelapproach was also used for some analyses, referred to here as Status ofUnivariates (SUn). In the SUn approach, all samples are initiallyevaluated using a standard multivariate model as described above. Next,univariate classification performance from single markers is used topotentially adjust the multivariate prediction score. If a particularsample's value for a given single marker is particularly high or low(i.e. in a score region of 100% positive or negative predictive value asassessed in the training set), the sample's probability score is changedto 0 or 1 accordingly. Overall, this approach enables augmentation ofthe complex multivariate models with simple high confidenceclassification calls based on individual markers.

After construction of the classifier model on the training set, it wasdirectly applied without modification to the testing set resulting inclassification scores for the held-out test set samples. After thecompletion of a complete 10-fold cross validation iteration, the testset classification scores from all the samples were merged into a singledataset or set of values, and the associated receiver operatingcharacteristic (ROC) curve was generated. From this ROC, the area underthe curve (AUC) was computed, with one AUC value for each of the 10iterations of 10-fold cross validation. The mean and median AUC's acrossthe 10 iterations was then used to assess the performance of theparticular classifier assembly process, representing an estimate of theanticipated hold-out set validation performance utilizing only thediscovery data.

To investigate the performance of different sized marker panels, avariety of feature selection and reduction methods were used includingElastic Network feature selection, Random Forest feature importanceranking, t-test p-value ranking, hierarchical clustering, and exhaustivecombination search. With the exception of exhaustive combination search,the feature selection methods were embedded within the individual foldsof the cross validation procedure to incorporate the variability ofmarker selection into the final performance assessment for a givenclassifier model build. For the exhaustive combination search, alln-choose-r combinations of features were evaluated, where a particularcombination was selected prior to model building and used across all thecross validation folds. For both computational feasibility reasons andto limit the possibility for over-fitting, n and r were chosen to haverelatively small values, with n typically <=30 total markers, and rtypically between 2 and 10.

Within the 10 by 10-fold cross validation folds and after the featureselection step, a classifier model was built using one of severalclassification algorithms including, as examples, the support vectormachine (SVM) algorithm, the Random Forest algorithm, Elastic Network(ENet) regression models with and without boosting, and k-nearestneighbors (kNN). The classification models were built using establishedclassification modeling packages implemented in the R statisticalprogramming language.

After construction of the classifier model on the training set, it wasdirectly applied without modification to the testing set resulting inclassification scores for the held-out test set samples. After thecompletion of a complete 10-fold cross validation iteration, the testset classification scores from all the samples were merged into a singleset of values and the associated receiver operating characteristic (ROC)curve was generated. From this ROC, the area under the curve (AUC) wascomputed, with one AUC value for each of the 10 iterations of 10-foldcross validation. The mean and median AUC's across the 10 iterations wasthen used to assess the performance of the particular classifierassembly process, representing an estimate of the anticipated hold-outset validation performance utilizing only the discovery data.

Classification Model Results

Utilizing the 10 by 10-fold cross validation procedure described above,a large number of classifier assembly methods were evaluated. Of thesemethods, 10 were selected for validation that provided the highestclassification performance across a range of different feature selectionand classification model methods. To validate a particular classifiermodel, a final model was built using all of the discovery data and thesame feature selection and classifier model methods used in theassociated 10 by 10-fold cross validation procedure. Each final modelconsisted of a set of markers and a classification model with associatedmodel parameters. This model was locked prior to validation and directlyapplied to the validation set with no addition tuning. A final ROC wasgenerated from the validation set classification scores, and the finalvalidation performance was measured via the AUC with 95% confidenceintervals on the ROC/AUC calculated from a bootstrap sampling procedure.

Table 7 provides a summary of the 10 classification models that werevalidated. Across the models, the discovery set AUC's range between 0.81and 0.86, and the validation AUC's range between 0.75 and 0.82. In allmodels except model 10, the discovery AUC's were within the 95%confidence intervals of the validation AUC indicating good validationwas achieved with the selected models.

The associated discovery and validation ROC curves are shown in FIGS.7A-18. Table 8 gives a summary of the 10 classification models that werevalidated. Across the models, the discovery set AUC's range between 0.81and 0.86, and the validation AUC's range between 0.75 and 0.82. In allmodels except model 10, the discovery AUC's were within the 95%confidence intervals of the validation AUC indicating good validationwas achieved with the selected models.

TABLE 8 Summary of 13 high performing models for CRC assessment.Validation Input Feature No. of Discovery Validation AUC Model DataSelection Classifier Features Proteins AUC (95% CI) 1 ELISA-28 + RandomRandom 7 A1AG1, A1AT, 0.84 0.80 Age Forest Forest CATD, CEA, (0.73-0.86)Interactions CO9, OSTPxAge, SEPR 2 ELISA-28 GLMNet SVM 17 A1AG1, A1AT,0.83 0.81 APOA1, CATD, (0.74-0.88) CEA, CLUS, CO3, CO9, FGB, FIBG, GARS,GELS, MIF, PRDX1, PSGL, SBP1, SEPR 3 ELISA-28 + GLMNet GLMNet 7 A1AG1,A1AT, 0.82 0.82 TP CATD, CEA, (0.75-0.88) CO9, GARS, SEPR 4 ELISA-28 +GLMNet GLMBoost 25 A1AG1, A1AT, 0.81 0.81 TP AACT, CATD, (0.74-0.88)CEA, CO9, CRP, AACT, CO9, CRP, CRP, CRP, CRP, CRP, CRP, GELS, S10A8,S10A8, S10A8, S10A8, S10A9, S10A9, GARS, SAA1, SEPR 5 ELISA-28 BruteForce SVM 8 CATD, CEA, 0.86 0.82 CO3, CO9, GARS, (0.75-0.88) GELS, SEPR,TFRC 6 ELISA-28 + Brute Force SVM 5 CATD, CEA, 0.86 0.80 TP AACT, CO9,(0.72-0.86) (Trace SEPR Classifi- cation Filtered) 7 ELISA-28 + GLMNet +SVM 10 A1AT, C3218600, 0.83 0.81 Unlabeled Top by p- C387796,(0.74-0.88) LCMS Value C597612, C979276, CATD, CEA, GARS, GELS, SEPR 8ELISA-28 GLMNet SVM + 18 A1AG1, A1AT, 0.84 0.78 SUn APOA1, CATD,(0.71-0.85) CEA, CLUS, CO3, CO9, FGB, FIBG, GARS, GELS, HPT, MIF, PRDX1,PSGL, SBP1, SEPR 9 ELISA-28 Random 2 SVM 11 A1AG1, A1AT, 0.85 0.80(Individual Forest Models CATD, CEA, (0.73-0.87) Features CO9, SEPR, andPair CATD/SEPR, Ratios) CATD/GELS, CO9/SEPR, A1AT/FIBG 10 ELISA-28 + H-GLMNet 41 H-Clustered 0.85 0.75 TP Clustering + Agglomerated (0.67-0.82)(Trace GLMNet Features Classifi- cation filtered) + SAT-29 11 ELISA-28+Brute SVM 8 CATD, CEA, 0.85 0.815 TP Force, CO3, CO9, (0.75-0.88) (model5 GARS S10A8, GELS, with Swap by SEPR, TFRC GARS Correlation featureswap) 12 ELISA-28 Brute SVM 8 AACT, CATD, 0.85 0.815 Force, CEA, CO3,CO9, (0.75-0.88) Protein MIF, PSGL, SEPR Subset 1 13 ELISA-28 Brute SVM7 A1AG, CATD, 0.86 0.80 Force, CEA, CO3, CO9, (0.73-0.87) Protein GELS,SEPR Subset 2

Model 1, as referenced in Table 8, included seven proteins which wereA1AG1, A1AT, CATD, CEA, CO9, OSTP, and SEPR. ROC curves resulting fromthe discovery set and the validation set for Model 1 are depicted inFIGS. 7A and 7B, respectively. The resulting discovery set AUC was 0.84and the validation set AUC was 0.80. At a validation ROC specificity of90%, the sensitivity is >50%, at a specificity of 75%, the sensitivityis >60%, and at a specificity of 50%, the sensitivity is >80%.

Model 2, as referenced in Table 8, included seven proteins which wereA1AG1, A1AT, APOA1, CATD, CEA, CLUS, CO3, CO9, FGB, FIBG, GARS, GELS,MIF, PRDX1, PSGL, SBP1, and SEPR. ROC curves resulting from thediscovery set and the validation set for Model 2 are depicted in FIGS.8A and 8B, respectively. The resulting discovery set AUC was 0.83 andthe validation set AUC was 0.81. At a validation ROC specificity of 90%,the sensitivity is about 50%, at a specificity of 75%, the sensitivityis >60%, and at a specificity of 50%, the sensitivity is >80%.

Model 3, as referenced in Table 8, included seven proteins which wereA1AG1, A1AT, CATD, CEA, CO9, GARS, and SEPR. ROC curves resulting fromthe discovery set and the validation set for Model 3 are depicted inFIGS. 9A and 9B, respectively. The resulting discovery set AUC was 0.82and the validation set AUC was 0.82. At a validation ROC specificity of90%, the sensitivity is >50%, at a specificity of 75%, the sensitivityis >70%, and at a specificity of 50%, the sensitivity is about 80%.

Model 4, as referenced in Table 8, included seven proteins which wereA1AG1, A1AT, AACT, CATD, CEA, CO9, CRP, GARS, GELS, S10A8, S10A9, SAA1,and SEPR. ROC curves resulting from the discovery set and the validationset for Model 4 are depicted in FIGS. 10A and 10B, respectively. Theresulting discovery set AUC was 0.81 and the validation set AUC was0.81. At a validation ROC specificity of 90%, the sensitivity is about60%, at a specificity of 75%, the sensitivity is >70%, and at aspecificity of 50%, the sensitivity is >80%.

Model 5, as referenced in Table 8, included seven proteins which wereCATD, CEA, CO3, CO9, GARS, GELS, SEPR, and TFRC. ROC curves resultingfrom the discovery set and the validation set for Model 5 are depictedin FIGS. 11A and 11B, respectively. The resulting discovery set AUC was0.86 and the validation set AUC was 0.82. At a validation ROCspecificity of 90%, the sensitivity is about 50%, at a specificity of75%, the sensitivity is >70%, and at a specificity of 50%, thesensitivity is about 90%.

Model 6, as referenced in Table 8, included seven proteins which wereCATD, CEA, AACT, CO9, and SEPR. ROC curves resulting from the discoveryset and the validation set for Model 6 are depicted in FIGS. 12A and12B, respectively. The resulting discovery set AUC was 0.86 and thevalidation set AUC was 0.80. At a validation ROC specificity of 90%, thesensitivity is >40%, at a specificity of 75%, the sensitivity is >60%,and at a specificity of 50%, the sensitivity is >80%.

Model 7, as referenced in Table 8, included seven proteins which wereA1AT, CATD, CEA, GARS, GELS, and SEPR. ROC curves resulting from thediscovery set and the validation set for Model 7 are depicted in FIGS.13A and 13B, respectively. The resulting discovery set AUC was 0.83 andthe validation set AUC was 0.81. At a validation ROC specificity of 90%,the sensitivity is >50%, at a specificity of 75%, the sensitivityis >60%, and at a specificity of 50%, the sensitivity is >80%.

Model 8, as referenced in Table 8, included seven proteins which wereA1AG1, A1AT, APOA1, CATD, CEA, CLUS, CO3, CO9, FGB, FIBG, GARS, GELS,HPT, MIF, PRDX1, PSGL, SBP1, and SEPR. ROC curves resulting from thediscovery set and the validation set for Model 8 are depicted in FIGS.14A and 14B, respectively. The resulting discovery set AUC was 0.84 andthe validation set AUC was 0.78. At a validation ROC specificity of 90%,the sensitivity is >30%, at a specificity of 75%, the sensitivityis >60%, and at a specificity of 50%, the sensitivity is >80%.

Model 9, as referenced in Table 8, included seven proteins which wereA1AG1, A1AT, CATD, CEA, CO9, FIBG, GELS, and SEPR. ROC curves resultingfrom the discovery set and the validation set for Model 9 are depictedin FIGS. 15A and 15B, respectively. The resulting discovery set AUC was0.85 and the validation set AUC was 0.80. At a validation ROCspecificity of 90%, the sensitivity is >50%, at a specificity of 75%,the sensitivity is >60%, and at a specificity of 50%, the sensitivity isabout 80%.

Model 11, as referenced in Table 8, included seven proteins which wereCATD, CEA, CO3, CO9, S10A8, GELS, SEPR, TFRC. The resulting discoveryset AUC was 0.85 and the validation set AUC was 0.82.

Model 12, as referenced in Table 8, included seven proteins which wereAACT, CATD, CEA, CO3, CO9, MIF, PSGL, SEPR. The resulting discovery setAUC was 0.85 and the validation set AUC was 0.82.

Model 13, as referenced in Table 8, included seven proteins which wereA1AG, CATD, CEA, CO3, CO9, GELS, SEPR. The resulting discovery set AUCwas 0.86 and the validation set AUC was 0.80,

Models 4 and 6 incorporated data from the targeted proteomics platform,and therefore included measurements from transition ions from specificpeptides from the underlying protein measurements. The transitions usedin these models are given in Table 9.

TABLE 9 Transition ions from specific peptides SEQ Model ID NumberProtein Peptide NO: Transition 4 AACT ADLSGITGAR 50 b3 4 CO9TEHYEEQIEAFK 51 y2 4 CRP ESDTSYVSLK 52 y3 4 CRP ESDTSYVSLK 52 y5 4 CRPGYSIFSYATK 53 y7 4 CRP GYSIFSYATK 53 y8 4 CRP KAFVFPK 54 y5 4 CRPKAFVFPK 54 y6 4 GELS AGALNSNDAFVLK 55 b4 4 S10A8 ALNSIIDVYHK 56 y6 4S10A8 ALNSIIDVYHK 56 y7 4 S10A8 GADVWFK 57 b3 4 S10A8 GADVWFK 57 y5 4S10A9 DLQNFLK 58 y5 4 S10A9 LGHPDTLNQGEFK 59 y10 6 AACT GKITDLIK 60 y5 6CO9 TEHYEEQIEAFK 51 y2

Of the ten models, model 5 is of particular note because of the highdiscovery AUC of 0.86 and associated high validation AUC of 0.82. Thismodel comprises 8 individual proteins all from a single assay platform(ELISA), facilitating the measurement of this marker panel for clinicalapplications.

Model 3 is also of interest because of the high validation AUC of 0.82,though the discovery AUC was slightly lower, also at 0.82. Thoughtargeted proteomics markers were included as input to this model, onlyELISA markers were selected in the final model. This panel is alsoslightly smaller, comprising 5 proteins.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

Incorporation of Indeterminate Classification Calls (NoC Method)

The intrinsic performance of a particular classification model dependson the distributions and separation of model scores for the two classes.With the rare exception of perfect class separation, most classificationmodels make mistakes because of class overlap across the range ofclassifier scores. For example, such an overlap may occur near themiddle of the score range where the probability of being in one class orthe other is close to 50%.

Within such an overlap region, it may be advantageous to add a thirdclass to the final set of classification calls; the third class wouldindicate the uncertainty of a call in this score region. This could beimplemented, for example, by defining an indeterminate region ofclassification scores. Samples with scores in this region would be givenan “indeterminate” or “no call” test result. Samples with scores aboveor below this region would be given standard positive or negative testresults depending on their positions relative to the test cutoff. Thebenefit of adding an indeterminate region to a classification model isthat classification performance can improve for samples outside of theindeterminate region, i.e. mistakes are less likely for the remainingpositive and negative tests. However, if the indeterminate range is toolarge, there may be too many indeterminate results, and the value of thetest may be put into question.

In another analysis, referred to here as NoC (“No Call”), the effect ofusing an indeterminate region with the classification models wasinvestigated. In this analysis, the percentage of samples targeted toreceive a “no call” result was set to 10%. To determine the optimalscore range for the indeterminate region (NoC region) with 10% of thesamples, the specificity was maximized at a sensitivity of >=90% asfollows: All possible contiguous sets of 10% of samples were determinedacross the classifier scores range. For each set, the associated set of10% of samples were marked as no calls. These samples were removed fromthe analysis set and the ROC curve was generated from the remaining 90%of the samples. The maximum specificity at >=90% sensitivity was thendetermined and used as the evaluation score for the NoC region inquestion. After all NoC regions were evaluated in this manner, theregion with the highest specificity score was selected as the optimalNoC region. The score range defining this NOC region was taken from theupper and lower classification scores of the associated 10% no callsamples.

To predict how the NoC procedure would affect classification performancein the hold-out validation set, the analysis was performed within the 10by 10-fold cross validation assessment of model 5 described above. Likeall previous model builds, only the discovery set was used in thisassessment. The resulting median AUC determined from this 10 by 10-foldvalidation procedure was 0.87, slightly higher than the originaldiscovery AUC of 0.86 without the application of NoC, suggesting the NoCprocedure could be beneficial to employ in practice.

A final NoC region was determined by using the same NoC proceduredescribed above on all of the discovery samples. This yielded a NoCregion encompassing scores between 0.298 and 0.396. This NoC region wasapplied directly to the validation set with 20 samples (13.3%) fallingwithin the region (10 disease, 10 control). The ROC determined from theremaining validation samples yielded an AUC of 0.85 (95% CI's:0.78-0.91), an improvement of 0.03 over the validation ROC withoutapplication of NoC. The results from the NoC analysis are given in Table10 and the discovery and validation ROC data in FIGS. 17A-17B.

TABLE 10 Summary of Model 5 with subset of samples categorized asindeterminate # of # of Samples Samples NoC in NoC Discovery Discoveryin NoC Validation Validation Score Region AUC AUC w/ Region AUC w/o AUCw/ Model Region Discovery w/o NoC NoC Validation NoC NoC 5 0.298- 150.86 0.87 20 0.82 0.85 0.396 

Comparing the ROC curves with and without NOC applied, NoC improvedperformance most in the region around 80%—60% specificity. With NOC, aclear improvement in sensitivity is apparent. In particular, the pointat 85% sensitivity and 78% specificity is of interest because of thegood overall performance for both sensitivity and specificity.

Selection of Classifier Cutoff Points

The overall performance of a classifier can be assessed via the AUC ofthe ROC as reported above. An ROC considers the performance of theclassifier at all possible model score cutoff points. However, when aclassification decision needs to be made (i.e. is this patient sick orhealthy?), a cutoff point must be used to define the two groups.Classification scores at or above the cutoff point are assessed aspositive (or sick) while points below are assessed as negative (orhealthy).

For the 10 classification models and the single model with NoC applied,summarized above, classification score cutoff points were established byselecting the point of maximum accuracy on the validation ROC's. Thepoint of maximum accuracy on an ROC is the cutoff point or points forwhich the total number of correct classification calls is maximized.Here, the positive and negative classification calls were weightedequally. In cases where multiple maximum accuracy points were present ona given ROC, the point with the associated maximum sensitivity wasselected.

The results for the cutoff point selection process are summarized inTable 11 and FIG. 13. The cutoff scores selected are representative ofthe type of score output by the associated model. For some models, theresulting classification score represents a probability and the scoresspan 0-1. For other models, e.g. Model 10, the classification score issimply a score, with larger scores more likely to represent CRCpatients. In these cases, the cutoff score can be greater than 1.

TABLE 11 Cutoff points for classification of a subject for colorectalcancer biomarker panels Model # Sensitivity Specificity Accuracy Cutoff 1 0.63 0.87 0.75 0.60  2 0.68 0.83 0.75 0.56  3 0.72 0.84 0.78 0.54  40.69 0.85 0.77 0.51  5 0.73 0.81 0.77 0.62  5 w/NoC 0.85 0.78 0.82 0.62 6 0.80 0.65 0.73 0.41  7 0.61 0.88 0.75 0.66  8 0.77 0.69 0.73 0.44  90.65 0.83 0.74 1.07 10 0.65 0.76 0.71 8.69

Advanced Adenoma Panel Combination

Advanced colorectal adenoma and CRC are assayed in parallel in somecases as described herein. For example a panel for colorectal cancer anda panel for advanced adenoma, having a single biomarker overlap at CATD,are measured in combination. In these embodiments a panel for diagnosingadvance adenoma may be derived using the methods previously disclosed.One panel for assessing a risk for advanced adenoma, and variants asdisclosed herein was derived using the steps of classification analysisfrom previous studies including the classification analysis on samplestaken from the Endoscopy II study.

For advanced adenoma biomarker discovery with the Endoscopy II study,302 samples selected for analysis comprised 151 control samples that hadno comorbidities and no adverse findings from colonoscopy, and 151disease samples that had confirmed colon or rectal adenoma lesions inadvanced stage. For this study, advanced colorectal adenomas weredefined as having at least one of the following: any adenoma >=1 cm,sessile serrated polyps >=1 cm, adenomas with high grade dysplasia, oradenomas with villous histological features. The control and diseasesamples were matched in pairs for age, gender, and enrollment site. The302 samples were further divided into discovery and validation sets,with 75 control and 75 advanced colorectal adenoma samples in thediscovery set, and 76 control and 76 advanced colorectal adenomassamples in the validation set. To more rigorously test thegeneralization performance of the investigated biomarker panels in thevalidation set, the discovery and validation sets consisted of patientsamples from non-overlapping sites. A summary table of the samples andtheir characteristics is provided in Table 12.

For data preparation, the 302 total samples were analyzed using ELISAassays for 30 different proteins, resulting in a concentrationmeasurement (e.g. accumulation level) for each of the 30 proteins acrossthe 302 samples. After data collection, the concentration values wereprepared in a variety of ways. For some analyses, the concentrationmeasurements were log 2 transformed, while for others, the concentrationvalues were left untransformed. Analyses were also performed onmeasurements that were both standardized (zero mean, unit variance) andun-standardized (i.e. original measurements).

Classification analysis was also performed. The goal of theclassification analysis was to determine the top performing markerpanels and classification models that distinguish between samples withand without advanced adenomas. Classifier models and the associatedclassification performance were assessed using a 10 by 10-fold crossvalidation procedure. The 10 by 10-fold cross validation was performedusing the discovery set only, and incorporated feature selection andclassification model assembly. In the cross validation procedure,feature selection was first applied to reduce the number of featuresused, followed by development of the classifier model and subsequentclassification performance evaluation. For each of the 10-fold crossvalidations, the data were segregated into 10 splits each containing 90%of the samples as a training set and the remaining 10% of the samples asa testing set. In this process, each sample was evaluated one time in atest set. The feature selection and model assembly was performed usingthe training set only, and these models were then applied to the testingset to evaluate classifier performance, typically via the area under thecurve (AUC) from the receiver operating characteristic (ROC) plot. Here,the mean or median AUC value obtained from each of the 10 10-fold crossvalidation procedures was used to assess the overall marker panel andclassification model performance.

To investigate the performance of different sized marker panels, avariety of feature selection and reduction methods were used includingElastic Network feature selection, Random Forest feature importanceranking, t-test p-value ranking, hierarchical clustering, and exhaustivecombination search. With the exception of exhaustive combination search,the feature selection methods were embedded within the individual foldsof the cross validation procedure to incorporate the variability ofmarker selection into the final performance assessment for a givenclassifier model build. For the exhaustive combination search, alln-choose-r combinations of features were evaluated, where a particularcombination was selected prior to model building and used across all thecross validation folds. For both computational feasibility reasons andto limit the possibility for over-fitting, n and r were chosen to haverelatively small values, with n typically <=30 total markers, and rtypically between 2 and 10.

Within the 10 by 10-fold cross validation folds and after the featureselection step, a classifier model was built using one of severalclassification algorithms including, as examples, the support vectormachine (SVM) algorithm, the Random Forest algorithm, Elastic Network(ENet) regression models with and without boosting, and k-nearestneighbors (kNN). The classification models were built using establishedclassification modeling packages implemented in the R statisticalprogramming language.

After construction of the classifier model on the training set, it wasdirectly applied without modification to the testing set resulting inclassification scores for the held-out test set samples. After thecompletion of a complete 10-fold cross validation iteration, the testset classification scores from all the samples were merged into a singleset of values and the associated receiver operating characteristic (ROC)curve was generated. From this ROC, the area under the curve (AUC) wascomputed, with one AUC value for each of the 10 iterations of 10-foldcross validation. The mean and median AUC's across the 10 iterations wasthen used to assess the performance of the particular classifierassembly process, representing an estimate of the anticipated hold-outset validation performance utilizing only the discovery data.

The classification model results were analyzed. Utilizing the 10 by10-fold cross validation procedure described above, a large number ofclassifier assembly methods were evaluated. Of these methods, one wasselected for validation that provided the highest classificationperformance across a range of different feature selection andclassification model methods. To validate this classifier model, a finalmodel was built using all of the discovery data and the same featureselection and classifier model methods used in the associated 10 by10-fold cross validation procedure. The final model consisted of a setof markers and a classification model with associated model parameters.This model was locked prior to validation and directly applied to thevalidation set with no addition tuning A final ROC was generated fromthe validation set classification scores, and the final validationperformance was measured via the AUC with 95% confidence intervals onthe ROC/AUC calculated from a bootstrap sampling procedure.

In sum, the AA model demonstrated the following parameters. The modelconsisted of 4 protein measurements from CATD, CLUS, GDF15 and SAA1. Themedian discovery AUC was 0.77 and AUC performance in the validation setwas 0.65. Despite the AUC drop from discovery to validation, the 95%confidence intervals on the ROC were 0.56 to 0.74 indicating that themodel provides classification discrimination significantly above randomperformance. The input data was ELISA-30 input and the classifier usedwas KNN.

The overall performance of a classifier is assessed in some cases viathe AUC of the ROC as reported herein. An ROC considers the performanceof the classifier at all possible model score cutoff points. However,when a classification decision needs to be made (e.g., is this patientsick or healthy?), a cutoff point is used to define the two groups.Classification scores at or above the cutoff point are assessed aspositive (or sick) while points below are assessed as negative (orhealthy) in various embodiments.

For some classification models disclosed herein, a classification scorecutoff point is established by selecting the point of maximum accuracyon the validation ROC. The point of maximum accuracy on an ROC is thecutoff point or points for which the total number of correctclassification calls is maximized. Here, the positive and negativeclassification calls are weighted equally. In cases where multiplemaximum accuracy points are present on a given ROC, the point with theassociated maximum sensitivity is selected in some cases. For some AApanels herein, the following parameters were observed: sensitivity of0.83, specificity of 0.45, accuracy of 0.64 and a cutoff of 0.25. Forsome AA panels herein, the following parameters were observed:sensitivity of 0.80 and specificity of 0.50.

Additional Reference to Figures

The disclosure herein is delineated throughout the specification andclaims appended herewith, supported by the figures. Referring to thefigures in more detail, one observes the following.

FIG. 1 depicts a workflow pipeline for the development of a lead CRCbiomarker panel. In box 1, at top, 28 best proteins are identified usinga targeted-mass spectrometry platform from 187 candidates compiled fromliterature. In box 2, a CRC test panel of 8 proteins is identified viamachine-learning in an unbiased, case-control study using ELISA. In box3, age as a biomarker is added to model as a parameter using a CRC vs.no comorbidities-no findings, case-control subset. In box 4,indeterminate call boundaries are added to the model using anintent-to-test patient subset. In box 5, at bottom, the 8 protein plusage classifier is validated using an intent-to-test patient subset.

FIG. 2 depicts a CRC panel AUC. The X axis indicates Specificity, atintervals of 20%, from 100% to 0%. The Y axis indicates Sensitivity, atintervals of 20%, from 0% to 100%. The slope along the diagonalindicates a 50% sensitivity and 50% Specificity. Shaded areas indicatethe 95% confidence interval for the graph. The dark curve indicatesperformance for the nine-member CRC panel comprising the proteins AACT,CO3, CO9, MIF, PSGL, CATD, CEA and SEPR, and the non-protein biomarkerof age. The AUC position corresponding to 81% sensitivity and 78%specificity is indicated. The performance is assessed using a 20%targeted indeterminate rate in discovery and a 15% validatedindeterminate rate.

Our study indicated that there was no significant difference in earlyverses late CRC performance. For CRC Stage I-II, there were 15 truevalues verses 5 false values with a sensitivity of 0.75. For CRC Stagethere were 15 true values verses 2 false values with a sensitivity of0.88. The average for both CRC Stage I-II results and CRC Stage III-IVresults was 15 true values and 7 false values with a sensitivity of0.81. The Fisher's Test p-value for this CRC stage assay was 0.415, andthe Chi-Square Test p-value was 0.546. No preferential class of sampleswas excluded in the indeterminate call group. Our study resultsindicated that for the no call group (NoC), the CRC class had 5 trueverses 37 false. The Non-CRC class had 51 true verses 280 false. Theaverage of the CRC class and nonCRC class NoC groups was 56 true verses317 false. For the NoC group, the Fisher's Test p-value for this assaywas 0.652, and the Chi-Square Test p-value was 0.712.

FIG. 3 depicts an AA panel AUC. The X axis indicates 1-Specificity, atintervals of 0.2, from 0.0 to 1.0. The Y axis indicates Sensitivity, atintervals of 0.2, from 0.0 to 1.0. The slope at x=y indicates a 50%sensitivity and 50% (1-Specificity). Shaded areas indicate the 95%confidence interval for the graph. The dark curve indicates performancefor the four-member panel, while the light grey lines indicateperformance of constituents.

FIG. 4 presents validation data for the CRC panel of FIG. 2. The CRCpanel is developed on a ‘Discovery 1’ sample collection, labeled ‘a’.The CRC panel is then re-derived and validated on a second sample set,divided into ‘Discovery 2,’ labeled ‘b,’ and a ‘Validation’ population,labeled ‘c’. As seen in FIG. 4, counts for columns b and c do not differsignificantly for any given category. This indicates that the CRC panel,as generated in the Discovery 1 set and recovered in the Discovery 2set, for a given category, was reliably validated. The close correlationbetween the discovery 2 and Validation results is an indication of therepeatability of the test. Columns are labeled, left to right, asfollows: Colon cancer, Rectal Cancer, No comorbidity—No finding,Adenoma—colon, Adenoma-rectum, Comorbidity—no finding, Other indication,and Other cancer.

FIG. 4 demonstrates that the CRC panel tested distinguishes not onlybetween CRC and healthy samples generally, but between CRC and non-CRCsamples, even those having other types of cancers. Accordingly, FIG. 4demonstrates that CRC panels disclosed herein distinguish CRC fromnon-CRC as indicated in circulating blood samples, even in samples fromindividuals suffering from other cancers.

FIG. 5 depicts Protein levels for CRC and healthy control samples forprotein markers relevant to the panels herein. For each protein, theleft or upper boxplot range indicates the control sample populationprotein level, and the right or lower boxplot indicates the CRC positivesample population protein level. Log 2 (concentration) ranges from 2-20across the top of the image. Proteins discussed herein are listed acrossthe left side of the image. The proteins in order are A1AG1, A1AT, AACT,ANAX1, APOA1, CAH1, CATD, CEA, CLUS, CO3, CO9, CRP, DPP4, FGB, FIBG,GARS, GDF15, GELS, HPT, MIF, OSTP, PKM, PRDX1, PSGL, SAA1, SBP1, SEPR,TFF3, TFRC, and TIMP1. FIG. 5 demonstrates that individual markers oftendo not vary substantially between CRC and healthy control samples,emphasizing the synergistic improvement of the biomarker panels aspresented herein over their individual biomarker constituents.

FIG. 6 depicts Protein levels for AA and healthy control samples forprotein markers relevant to the panels herein. For each protein, theleft or upper boxplot range indicates the control sample populationprotein level, and the right or lower boxplot indicates the CRC positivesample population protein level. Log 2 (concentration) ranges from 2-20across the top of the image. Proteins discussed herein are listed acrossthe left side of the image. The proteins in order are A1AG1, A1AT, AACT,ANAX1, APOA1, CAH1, CATD, CEA, CLUS, CO3, CO9, CRP, DPP4, FGB, FIBG,GARS, GDF15, GELS, HPT, MIF, OSTP, PKM, PRDX1, PSGL, SAA1, SBP1, SEPR,TFF3, TFRC, and TIMP1. FIG. 5 demonstrates that individual markers oftendo not vary substantially between AA and healthy control samples,emphasizing the synergistic improvement of the biomarker panels aspresented herein over their individual biomarker constituents.

FIGS. 7A-16B present Discovery and Validation AUC plots for Panel Models1-10 as presented herein. For each figure, the X axis indicatesSpecificity, at intervals of 20%, from 0% to 100%, or alternately1-Specificity, at intervals of 0.2, from 0.0 to 1.0. The Y axisindicates Sensitivity, at intervals of 20%, from 0% to 100%. The slopealong the diagonal indicates a 50% sensitivity and 50% Specificity. Thebox-plot indicated the 95% confidence interval for the graph.

Model 1 included A1AG1, A1AT, CATD, CEA, CO9, OSTP, and SEPR. ROC curvesresulting from the discovery set and the validation set for Model 1 aredepicted in FIGS. 7A and 7B, respectively. The resulting discovery setAUC was 0.84 and the validation set AUC was 0.80. At a validation ROCspecificity of 90%, the sensitivity is >50%, at a specificity of 75%,the sensitivity is >60%, and at a specificity of 50%, the sensitivityis >80%. Model 2 included A1AG1, A1AT, APOA1, CATD, CEA, CLUS, CO3, CO9,FGB, FIBG, GARS, GELS, MIF, PRDX1, PSGL, SBP1, and SEPR. ROC curvesresulting from the discovery set and the validation set for Model 2 aredepicted in FIGS. 8A and 8B, respectively. The resulting discovery setAUC was 0.83 and the validation set AUC was 0.81. At a validation ROCspecificity of 90%, the sensitivity is about 50%, at a specificity of75%, the sensitivity is >60%, and at a specificity of 50%, thesensitivity is >80%. Model 3 included A1AG1, A1AT, CATD, CEA, CO9, GARS,and SEPR. ROC curves resulting from the discovery set and the validationset for Model 3 are depicted in FIGS. 9A and 9B, respectively. Theresulting discovery set AUC was 0.82 and the validation set AUC was0.82. At a validation ROC specificity of 90%, the sensitivity is >50%,at a specificity of 75%, the sensitivity is >70%, and at a specificityof 50%, the sensitivity is about 80%. Model 4 included A1AG1, A1AT,AACT, CATD, CEA, CO9, CRP, GARS, GELS, S10A8, S10A9, SAM, and SEPR. ROCcurves resulting from the discovery set and the validation set for Model4 are depicted in FIGS. 10A and 10B, respectively. The resultingdiscovery set AUC was 0.81 and the validation set AUC was 0.81. At avalidation ROC specificity of 90%, the sensitivity is about 60%, at aspecificity of 75%, the sensitivity is >70%, and at a specificity of50%, the sensitivity is >80%. Model 5 included CATD, CEA, CO3, CO9,GARS, GELS, SEPR, and TFRC. ROC curves resulting from the discovery setand the validation set for Model 5 are depicted in FIGS. 11A and 11B,respectively. The resulting discovery set AUC was 0.86 and thevalidation set AUC was 0.82. At a validation ROC specificity of 90%, thesensitivity is about 50%, at a specificity of 75%, the sensitivityis >70%, and at a specificity of 50%, the sensitivity is about 90%.Model 6 included seven proteins which were CATD, CEA, AACT, CO9, andSEPR. ROC curves resulting from the discovery set and the validation setfor Model 6 are depicted in FIGS. 12A and 12B, respectively. Theresulting discovery set AUC was 0.86 and the validation set AUC was0.80. At a validation ROC specificity of 90%, the sensitivity is >40%,at a specificity of 75%, the sensitivity is >60%, and at a specificityof 50%, the sensitivity is >80%. Model 7, as referenced in Table 5included seven proteins which were A1AT, CATD, CEA, GARS, GELS, andSEPR. ROC curves resulting from the discovery set and the validation setfor Model 7 are depicted in FIGS. 13A and 13B, respectively. Theresulting discovery set AUC was 0.83 and the validation set AUC was0.81. At a validation ROC specificity of 90%, the sensitivity is >50%,at a specificity of 75%, the sensitivity is >60%, and at a specificityof 50%, the sensitivity is >80%. Model 8, as referenced in Table 5,included A1AG1, A1AT, APOA1, CATD, CEA, CLUS, CO3, CO9, FGB, FIBG, GARS,GELS, HPT, MIF, PRDX1, PSGL, SBP1, and SEPR.

ROC curves resulting from the discovery set and the validation set forModel 8 are depicted in FIGS. 14A and 14B, respectively. The resultingdiscovery set AUC was 0.84 and the validation set AUC was 0.78. At avalidation ROC specificity of 90%, the sensitivity is >30%, at aspecificity of 75%, the sensitivity is >60%, and at a specificity of50%, the sensitivity is >80%. Model 9 included A1AG1, A1AT, CATD, CEA,CO9, FIBG, GELS, and SEPR. ROC curves resulting from the discovery setand the validation set for Model 9 are depicted in FIGS. 15A and 15B,respectively. The resulting discovery set AUC was 0.85 and thevalidation set AUC was 0.80. At a validation ROC specificity of 90%, thesensitivity is >50%, at a specificity of 75%, the sensitivity is >60%,and at a specificity of 50%, the sensitivity is about 80%. Model 10curves resulting from the discovery set and the validation set for Model10 are depicted in FIGS. 16A and 16B, respectively. The resultingdiscovery set AUC was 0.85 and the validation set AUC was 0.75.

FIGS. 17A-17B depict an alternate analysis of Model 5 using ‘NOC’analysis. The X axis indicates Specificity, at intervals of 20%, from100% to 0%. The Y axis indicates Sensitivity, at intervals of 20%, from0% to 100%. The slope along the diagonal indicates a 50% sensitivity and50% Specificity. The box-plot indicated the 95% confidence interval forthe graph.

In this analysis, referred to here as NoC (“No Call”), the effect ofusing an indeterminate region with the classification models wasinvestigated. In this analysis, the percentage of samples targeted toreceive a “no call” result was set to 10%. To determine the optimalscore range for the indeterminate region (NoC region) with 10% of thesamples, the specificity was maximized at a sensitivity of >=90% asfollows: All possible contiguous sets of 10% of samples were determinedacross the classifier scores range. For each set, the associated set of10% of samples were marked as no calls. These samples were removed fromthe analysis set and the ROCcurve was generated from the remaining 90%of the samples. The maximum specificity at >=90% sensitivity was thendetermined and used as the evaluation score for the NoC region inquestion. After all NoC regions were evaluated in this manner, theregion with the highest specificity score was selected as the optimalNoC region. The score range defining this NOC region was taken from theupper and lower classification scores of the associated 10% no callsamples. To predict how the NoC procedure would affect classificationperformance in the hold-out validation set, the analysis was performedwithin the 10 by 10-fold cross validation assessment of model 5described above. Like all previous model builds, only the discovery setwas used in this assessment. The resulting median AUC determined fromthis 10 by 10-fold validation procedure was 0.87, slightly higher thanthe original discovery AUC of 0.86 without the application of NoC,suggesting the NoC procedure could be beneficial to employ in practice.

A final NoC region was determined by using the same NoC proceduredescribed above on all of the discovery samples. This yielded a NoCregion encompassing scores between 0.298 and 0.396. This NoC region wasapplied directly to the validation set with 20 samples (13.3%) fallingwithin the region (10 disease, 10 control). The ROC determined from theremaining validation samples yielded an AUC of 0.85 (95% CI's:0.78-0.91), an improvement of 0.03 over the validation ROC withoutapplication of NoC.

Comparing the ROC curves with and without NOC applied, NoC improvedperformance most in the region around 80%—60% specificity. With NOC, aclear improvement in sensitivity is apparent. In particular, the pointat 85% sensitivity and 78% specificity is of interest because of thegood overall performance for both sensitivity and specificity.

FIG. 17B depicts further NOC analysis results. The X axis indicates1-Specificity, at intervals of 0.2, from 0.0 to 1.0. The Y axisindicates Sensitivity, at intervals of 0.2, from 0.0 to 1.0. The slopeat x=y indicates a 50% sensitivity and 50% (1-Specificity). Shaded areasindicate the 95% confidence interval for the graph. The dark curveindicates performance for the four-member panel, while the light greylines indicate performance of constituents.

FIG. 18 depicts Sensitivity and Specificity for Models 1-10 at the pointof their AUCs corresponding to Maximum Accuracy. Sensitivity, on the Yaxis, ranges from 0-1 in intervals of 0.25. The X axis depicts1-Specificity, ranging from 0 to 1 in intervals of 0.25. Models 1-10 arelabeled a-k, respectively.

FIG. 19 depicts a Computer System consistent with the methods, systems,kits and compositions disclosed herein.

FIG. 20 depicts AUC values for randomly generated panels from abiomarker set enriched to be predictive of CRC. The mean and median AUCvalues are well below those of the CRC panels disclosed herein.

NUMBERED EMBODIMENTS

The disclosure is further understood through review of the numberedembodiments recited herein. 1. An ex vivo method of assessing acolorectal cancer risk status in a blood sample of an individual,comprising the steps of obtaining a circulating blood sample from theindividual; obtaining a biomarker panel level for a biomarker panelcomprising a list of proteins in the sample comprising AACT, CO3, CO9,MIF, and PSGL to comprise panel information from said individual;comparing said panel information from said individual to a referencepanel information set corresponding to a known colorectal cancer status;and categorizing said individual as having said colorectal cancer riskstatus if said individual's reference panel information does not differsignificantly from said reference panel information set. 2. The methodof embodiment 1, wherein obtaining a circulating blood sample comprisesdrawing blood from a vein or artery of the individual. 3. The method ofany one of embodiments 1-2, wherein the panel information comprises ageinformation for the individual. 4. The method of any one of embodiments1-3, wherein the list of proteins comprises AACT, CO3, CO9, MIF, PSGL,CATD, CEA and SEPR. 5. The method of any one of embodiments 1-4, whereinthe list of proteins comprises no more than 15 proteins. 6. The methodof any one of embodiments 1-5, wherein the list of proteins comprises nomore than 8 proteins. 7. The method of any one of embodiments 1-6,wherein the list of proteins comprises AACT, CO3, CO9, MIF, PSGL, CATD,CEA and SEPR. 8. The method of any one of embodiments 1-7, wherein thecategorizing has a sensitivity of at least 81% and a specificity of atleast 78%. 9. The method of any one of embodiments 1-8, comprisingtransmitting a report of results of said categorizing a healthpractitioner. 10. The method of any one of embodiments 1-9, wherein thereport indicates a sensitivity of at least 81%. 11. The method of anyone of embodiments 1-9, wherein the report indicates a specificity of atleast 78%. 12. The method of any one of embodiments 1-9, wherein thereport recommends that a colonoscopy be performed. 13. The method of anyone of embodiments 1-12, comprising performing a colonoscopy on theindividual. 14. The method of any one of embodiments 1-9, wherein thereport recommends an independent surgical intervention. 15. The methodof any one of embodiments 1-14, comprising performing an independentsurgical intervention on the individual. 16. The method of any one ofembodiments 1-9, wherein the report recommends undergoing an independentcancer assay. 17. The method of any one of embodiments 1-16, comprisingperforming an independent cancer assay on the individual. 18. The methodof any one of embodiments 1-9, wherein the report recommends undergoinga stool cancer assay. 19. The method of any one of embodiments 1-18,comprising performing a stool cancer assay. 20. The method of any one ofembodiments 1-9, wherein the report recommends administering ananticancer composition. 21. The method of any one of embodiments 1-18,comprising administering an anticancer composition. 22. The method ofany one of embodiments 1-9, wherein the report recommends continuedmonitoring. 23. The method of any one of embodiments 1-22, wherein atleast one biomarker level of said individual's panel information differssignificantly from a corresponding value from said reference panel, andwherein said individual's panel level as a whole does not differsignificantly from said reference panel level. 24. The method of any oneof embodiments 1-23, wherein no parameter of said individual's referencepanel information in isolation is indicative of said colorectal cancerstatus in said individual at a sensitivity of greater than 65% or aspecificity of greater than 65%. 25. The method of any one ofembodiments 1-24, wherein the obtaining protein levels comprisescontacting a fraction of the circulating blood sample to a set ofantibodies, wherein the set of antibodies comprises antibodies specificto AACT, CO3, CO9, MIF, and PSGL. 26. The method of any one ofembodiments 1-25, wherein the obtaining protein levels comprisessubjecting a fraction of the circulating blood sample to a massspectrometric analysis. 27. The method of any one of embodiments 1-26,wherein at least one of said comparing and said categorizing isperformed on a computer configured to analyze reference panelinformation. 28. The method of any one of embodiments 1-27, wherein saidreference panel information set corresponding to a known colorectalcancer status comprises a product of a machine learning model. 29. Themethod of any one of embodiments 1-28, wherein the machine learningmodel is trained using at least 100 biomarker panels corresponding toknown colorectal health status. 30. An ex vivo method of monitoringefficacy of a colorectal cancer treatment in an individual, comprisingthe steps of obtaining a first sample comprising circulating blood fromthe individual at a first time point; obtaining a second samplecomprising circulating blood from the same individual receiving acolorectal cancer treatment at a second time point; obtaining a firstpanel level comprising protein levels for a list of proteins in thefirst sample and a second panel level comprising protein levels for alist of proteins in the second sample, said list comprising AACT, CO3,CO9, MIF, and PSGL to comprise panel information for said first sampleand said second sample; wherein a change in protein levels indicatesefficacy of the colorectal cancer treatment. 31. The method ofembodiment 30, wherein obtaining the first sample comprises drawingblood from a vein or artery of the individual. 32. The method of any oneof embodiments 30-31, wherein the colorectal cancer treatment comprisesadministration of a pharmaceutical composition. 33. The method of anyone of embodiments 30-32, wherein the colorectal cancer treatmentcomprises administration of a chemotherapeutic agent. 34. The method ofany one of embodiments 30-33, wherein the colorectal cancer treatmentcomprises a colonoscopy. 35. The method of any one of embodiments 30-34,wherein the colorectal cancer treatment comprises a polypectomy. 36. Themethod of any one of embodiments 30-35, wherein the colorectal cancertreatment comprises radiotherapy. 37. The method of any one ofembodiments 30-36, comprising comparing said first sample panel leveland said second panel level to at least one panel level of a healthyreference, wherein the second sample panel level being more similar tothe panel level of the healthy reference indicates efficacy of thecolorectal cancer treatment. 38. The method of any one of embodiments30-37, comprising said first sample panel level and said second panellevel to at least one panel level of a healthy reference, wherein thefirst sample panel level being more similar to the panel level of thecolorectal cancer reference indicates efficacy of the colorectal cancertreatment. 39. The method of any one of embodiments 30-38, wherein thelist of proteins comprises AACT, CO3, CO9, MIF, PSGL, CATD, CEA andSEPR. 40. The method of any one of embodiments 30-39, wherein the listof proteins comprises no more than 15 proteins. 41. The method of anyone of embodiments 30-40, wherein the list of proteins comprises no morethan 8 proteins. 42. The method of any one of embodiments 30-41, whereinthe list of proteins comprises AACT, CO3, CO9, MIF, PSGL, CATD, CEA andSEPR. 43. The method of any one of embodiments 30 to 42, comprisingchanging the colorectal cancer treatment if no efficacy is indicated.44. The method of any one of embodiments 30 to 42, comprising repeatingthe colorectal cancer treatment if no efficacy is indicated. 45. Themethod of any one of embodiments 30 to 42, comprising continuing thecolorectal cancer treatment if no efficacy is indicated. 46. The methodof any one of embodiments 30 to 42, comprising discontinuing thecolorectal cancer treatment if efficacy is indicated. 47. A panel ofproteins indicative of an individual's colorectal cancer status,comprising at least 5 proteins selected from the list consisting ofAACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR, wherein measurement ofthe panel at a level that does not differ significantly from a referencepanel from circulating blood of an individual is indicative of theindividual's colorectal cancer status corresponding to a reference panelcolorectal cancer status at a sensitivity of at least 81% and aspecificity of at least 78%; and wherein no constituent protein level ofsaid panel is indicative of the individual's colorectal cancer status ata sensitivity of greater than 65% and a specificity of greater than 65%.48. The panel of embodiment 47, comprising at least 6 proteins selectedfrom the list consisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA andSEPR. 49. The panel of any one of embodiments 47-48, comprising no morethan 12 proteins, of which at least 4 proteins selected from the listconsisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR. 50. Thepanel of any one of embodiments 47-49, comprising no more than 12proteins, wherein the panel of proteins comprises AACT, CO3, CO9, MIF,PSGL, CATD, CEA and SEPR. 51. The panel of any one of embodiments 47-50,consisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR. 52. Thepanel of proteins according to any one of embodiments 47-51, for use ina method of assessing a colorectal cancer status according to any one ofembodiments 1-29, or for use in a method of monitoring efficacy of acolorectal cancer treatment according to any one of embodiments 30-46.53. A kit comprising an antibody panel, said antibody panel comprisingantibodies that identify at least 5 proteins selected from the listconsisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR. 54. The kitof embodiment 53, comprising an antibody that binds to a controlprotein. 55. The kit of any one of embodiments 53-54, wherein saidantibody panel comprises no more than 15 antibodies. 56. The kit of anyone of embodiments 53-55, wherein said antibody panel comprises no morethan 12 antibodies. 57. The kit of any one of embodiments 53-56, whereinsaid antibody panel comprises antibodies that identify all of AACT, CO3,CO9, MIF, PSGL, CATD, CEA and SEPR. 58. The kit of any one ofembodiments 53-57, comprising instructions functionally related to useof the kit to assess a patient colorectal cancer status. 59. The kitcomprising an antibody panel according to any one of embodiments 47-52,for use in a method of assessing a colorectal cancer status according toany one of embodiments 1-29, or for use in a method of monitoringefficacy of a colorectal cancer treatment according to any one ofembodiments 30-46. 60. A computer system configured to assess acolorectal cancer risk in an individual, said computer system comprisingA memory unit for receiving data comprising measurement of a panel ofproteins comprising at least 5 proteins selected from the listconsisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR from abiological sample comprising circulating blood Computer-executableinstructions for assessing a colorectal cancer risk associated with saidmeasurement of said panel of proteins An output unit for delivering areport assessing said colorectal cancer risk associated with saidmeasurement of said panel of proteins. 61. The computer system ofembodiment 60, wherein said panel comprises at least 6 proteins selectedfrom the list consisting of AACT, CO3, CO9, MIF, PSGL, CATD, CEA andSEPR. 62. The computer system of any one of embodiments 60-61, whereinsaid panel comprises no more than 12 proteins, of which at least 5proteins selected from the list consisting of AACT, CO3, CO9, MIF, PSGL,CATD, CEA and SEPR. 63. The computer system of any one of embodiments60-62, wherein said panel comprises no more than 12 proteins, whereinthe panel of proteins comprises AACT, CO3, CO9, MIF, PSGL, CATD, CEA andSEPR. 64. The computer system of any one of embodiments 60-63, whereinsaid panel consists of AACT, CO3, CO9, MIF, PSGL, CATD, CEA and SEPR.65. The computer system of any one of embodiments 60-64, wherein thememory unit is configured for receiving data comprising measurement of asecond panel of proteins. 66. The computer system of any one ofembodiments 60-65, wherein said data comprising measurement of a panelof proteins comprises ELISA data. 67. The computer system of any one ofembodiments 60-66, wherein said data comprising measurement of a panelof proteins comprises mass spectrometry data. 68. The computer system ofany one of embodiments 60-67, wherein assessing a colorectal cancer riskcomprises comparing said data to a reference panel associated with aknown colorectal cancer status. 69. The computer system of any one ofembodiments 60-68, wherein said individual is assigned said knowncolorectal cancer status when said data does not differ significantlyfrom said reference panel. 70. The computer system of any one ofembodiments 60-68, wherein said reference panel indicates presence ofcolorectal cancer. 71. The computer system of any one of embodiments60-68, wherein said reference panel indicates absence of colorectalcancer. 72. The computer system of any one of embodiments 60-71, whereinassessing a colorectal cancer risk is performed on a computer configuredto analyze reference panel information. 73. The computer system of anyone of embodiments 60-72, wherein said memory unit comprises at leastone reference panel information set corresponding to a known colorectalcancer status. 74. The computer system of any one of embodiments 60-73,wherein the at least one reference panel information set comprises amachine learning model. 75. The computer system of any one ofembodiments 60-74, wherein the machine learning model is trained usingat least 100 biomarker panels corresponding to known colorectal healthstatus. 76. The computer system of any one of embodiments 60-75, whereinsaid report indicates a sensitivity of at least 81% and a specificity ofat least 78%. 77. The computer system of any one of embodiments 60-76,wherein said report indicates a sensitivity of at least 81%. 78. Thecomputer system of any one of embodiments 60-77, wherein said reportindicates a specificity of at least 78%. 79. The computer system of anyone of embodiments 60-78, wherein said report recommends that acolonoscopy be performed. 80. The computer system of any one ofembodiments 60-79, wherein said report recommends an independentsurgical intervention. 81. The computer system of any one of embodiments60-80, wherein said report recommends undergoing an independent cancerassay. 82. The computer system of any one of embodiments 60-81, whereinsaid report recommends undergoing a stool cancer assay. 83. The computersystem of any one of embodiments 60-82, wherein said report recommendsadministering an anticancer composition. 84. The computer system of anyone of embodiments 60-83, wherein said report recommends continuedmonitoring. 85. The computer system of any one of embodiments 60-84,wherein at least one parameter of said individual's reference panelinformation differs significantly from a corresponding value from saidreference panel information set, and wherein said individual's referencepanel information does not differ significantly from said referencepanel information set. 86. The computer system of any one of embodiments60-85, wherein no single protein of said panel indicates theindividual's colorectal cancer status at a specificity of greater than65% or a sensitivity of greater than 65%. 87. The computer system of anyone of embodiments 60-86, wherein the memory unit is configured toreceive age information from said individual. 88. The computer system ofany one of embodiments 60-87, wherein the computer-executableinstructions factor in age of the individual when assessing saidcolorectal cancer risk associated with said measurement of said panel ofproteins. 89. An ex vivo method of assessing an advanced adenoma riskstatus in a blood sample of an individual, comprising the steps ofobtaining a circulating blood sample from the individual; obtainingprotein levels for a list of proteins relevant to advanced adenoma inthe sample comprising at least three of CATD, CLUS, GDF15 and SAA1 tocomprise biomarker panel information from said individual; comparingsaid panel information from said individual to a reference panelinformation set corresponding to a known advanced adenoma status; andcategorizing said individual as having said advanced adenoma risk statusif said individual's reference panel information does not differsignificantly from said reference panel information set. 90. The methodof any one of embodiments 89, wherein obtaining a circulating bloodsample comprises drawing blood from a vein or artery of the individual91. The method of any one of embodiments 89-90, wherein the panelinformation comprises age information for the individual. 92. The methodof any one of embodiments 89-91, wherein the list of proteins comprisesno more than 15 proteins. 93. The method of any one of embodiments89-92, wherein the list of proteins comprises no more than 5 proteins.94. The method of any one of embodiments 89-93, wherein the list ofproteins comprises CATD, CLUS, GDF15 and SAA1. 95. The method of any oneof embodiments 89-94, wherein the categorizing has a sensitivity of atleast 50% and a specificity of at least 80%. 96. The method of any oneof embodiments 89-95, comprising transmitting a report of results ofsaid categorizing to a healthcare professional. 97. The method of anyone of embodiments 89-96, wherein the report indicates a sensitivity ofat least 50%. 98. The method of any one of embodiments 89-96, whereinthe report indicates a specificity of at least 80%. 99. The method ofany one of embodiments 89-96, wherein the report recommends that acolonoscopy be performed. 100. The method of any one of embodiments89-99, wherein the individual undergoes a colonoscopy. 101. The methodof any one of embodiments 89-96, wherein the report recommends anindependent surgical intervention. 102. The method of any one ofembodiments 89-101, wherein the individual undergoes an independentsurgical intervention. 103. The method of any one of embodiments 89-96,wherein the report recommends undergoing an independent cancer assay.104. The method of any one of embodiments 89-103, wherein the individualundergoes an independent cancer assay. 105. The method of any one ofembodiments 89-96, wherein the report recommends undergoing a stoolcancer assay. 106. The method of any one of embodiments 89-105, whereinthe individual undergoes a stool cancer assay. 107. The method of anyone of embodiments 89-96, wherein the report recommends administering ananticancer composition. 108. The method of any one of embodiments89-107, wherein an anticancer composition is administered to theindividual. 109. The method of any one of embodiments 89-96, wherein thereport recommends continued monitoring. 110. The method of any one ofembodiments 89-109, wherein at least one parameter of said individual'sreference panel differs significantly from a corresponding value fromsaid reference panel set, and wherein said individual's reference panelinformation as a whole does not differ significantly from said referencepanel information set. 111. The method of any one of embodiments 89-110,wherein no parameter of said individual's reference panel information inisolation is indicative of said advanced adenoma status in saidindividual at a sensitivity of greater than 65% or a specificity ofgreater than 65%. 112. The method of any one of embodiments 89-111,wherein the obtaining protein levels comprises contacting a fraction ofthe circulating blood sample to a set of antibodies, wherein the set ofantibodies comprises antibodies specific to CATD, CLUS, GDF15 and SAA1.113. The method of any one of embodiments 89-112, wherein the obtainingprotein levels comprises subjecting a fraction of the circulating bloodsample to a mass spectrometric analysis. 114. The method of any one ofembodiments 89-113, wherein the obtaining protein levels comprisescontacting the sample to protein binding DNA aptamers. 115. The methodof any one of embodiments 89-114, wherein the obtaining protein levelscomprises contacting the sample to an antibody array. 116. The method ofany one of embodiments 89-115, wherein at least one of said comparingand said categorizing is performed on a computer configured to analyzereference panel information. 117. The method of any one of embodiments89-116, wherein said reference panel information set corresponding to aknown advanced adenoma status comprises is a product of a machinelearning model. 118. The method of any one of embodiments 89-117,wherein the machine learning model is trained using at least 100biomarker panels corresponding to known colorectal health status. 119.An ex vivo method of monitoring efficacy of an advanced adenomatreatment in an individual, comprising the steps of obtaining a firstsample comprising circulating blood from the individual at a first timepoint; obtaining a second sample comprising circulating blood from thesame individual receiving an advanced adenoma treatment at a second timepoint; obtaining a first panel level protein levels for a list ofproteins relevant to advanced adenoma assessment in the first sample anda second panel level protein levels for a list of proteins relevant toadvanced adenoma assessment in the second sample, said list comprisingCATD, CLUS, GDF15 and SAA1 to comprise panel information for said firstsample and said second sample; wherein a change in protein levelsindicates efficacy of the advanced adenoma treatment. 120. The method ofembodiment 119, wherein obtaining the first sample comprises drawingblood from a vein or artery of the individual. 121. The method of anyone of embodiments 119-120, wherein the advanced adenoma treatmentcomprises administration of a pharmaceutical composition. 122. Themethod of any one of embodiments 119-121, wherein the advanced adenomatreatment comprises administration of a chemotherapeutic agent. 123. Themethod of any one of embodiments 119-122, wherein the advanced adenomatreatment comprises a colonoscopy. 124. The method of any one ofembodiments 119-123, wherein the advanced adenoma treatment comprises apolypectomy. 125. The method of any one of embodiments 119-124, whereinthe advanced adenoma treatment comprises radiotherapy. 126. The methodof any one of embodiments 119-125, comprising comparing said firstsample protein levels and said second panel protein levels to proteinlevels of a healthy reference, wherein the second sample levels beingmore similar to the protein levels of the healthy reference indicatesefficacy of the advanced adenoma treatment. 127. The method of any oneof embodiments 119-126, comprising comparing said first sample proteinlevels and said second panel protein levels to protein levels of anadvanced adenoma reference, wherein the first sample levels being moresimilar to the protein levels of the advanced adenoma referenceindicates efficacy of the advanced adenoma treatment. 128. The method ofany one of embodiments 119-127, wherein the list of proteins relevant toadvanced adenoma assessment comprises CATD, CLUS, GDF15 and SAA1. 129.The method of any one of embodiments 119-128, wherein the list ofproteins relevant to advanced adenoma assessment comprises no more than12 proteins. 130. The method of any one of embodiments 119-129, whereinthe list of proteins relevant to advanced adenoma assessment comprisesno more than 8 proteins. 131. The method of any one of embodiments119-130, wherein the list of proteins relevant to advanced adenomaassessment consists of CATD, CLUS, GDF15 and SAA1. 132. The method ofany one of embodiments 119 to 131, comprising changing the advancedadenoma treatment if no efficacy is indicated. 133. The method of anyone of embodiments 119 to 131, comprising repeating the advanced adenomatreatment if no efficacy is indicated. 134. The method of any one ofembodiments 119 to 131, comprising continuing the advanced adenomatreatment if no efficacy is indicated. 135. The method of any one ofembodiments 119 to 131, comprising discontinuing the advanced adenomatreatment if efficacy is indicated. 136. A panel of proteins indicativeof an individual's advanced adenoma status, comprising at least 3proteins relevant to advanced adenoma assessment selected from the listconsisting of CATD, CLUS, GDF15 and SAA1, wherein measurement of thepanel at a level that does not differ significantly from a referencepanel from circulating blood of an individual is indicative of theindividual's advanced adenoma status corresponding to a reference paneladvanced adenoma status at a sensitivity of at least 50% and aspecificity of at least 80%; and wherein no constituent protein level ofsaid panel is indicative of the individual's advanced adenoma status ata sensitivity of greater than 65% and a specificity of greater than 65%.137. The panel of embodiment 136, comprising proteins relevant toadvanced adenoma assessment CATD, CLUS, GDF15 and SAA1. 138. The panelof proteins according to any one of embodiments 136-137, for use in amethod of assessing an advanced adenoma status according to any one ofembodiments 89-119, or for use in a method of monitoring efficacy of anadvanced adenoma treatment according to any one of embodiments 120-136.139. A kit comprising an antibody panel, said antibody panel comprisingantibodies that identify at least 3 proteins relevant to advancedadenoma assessment selected from the list consisting of CATD, CLUS,GDF15 and SAA1. 140. The kit of any one of embodiments 139, comprisingan antibody that binds to a control protein. 141. The kit of any one ofembodiments 139-140, wherein said antibody panel comprises no more than15 antibodies. 142. The kit of any one of embodiments 139-141, whereinsaid antibody panel comprises no more than 12 antibodies. 143. The kitof any one of embodiments 139-142, wherein said antibody panel comprisesantibodies that identify all of CATD, CLUS, GDF15 and SAA1. 144. The kitof any one of embodiments 139-143, comprising instructions functionallyrelated to use of the kit to assess a patient advanced adenoma status.145. The kit comprising an antibody panel according to any one ofembodiments 136-138, for use in a method of assessing an advancedadenoma status according to any one of embodiments 89-119, or for use ina method of monitoring efficacy of an advanced adenoma treatmentaccording to any one of embodiments 120-136. 146. A computer systemconfigured to assess an advanced adenoma risk in an individual, saidcomputer system comprising A memory unit for receiving data comprisingmeasurement of a panel of proteins comprising at least 3 proteinsindicative of an individual's advanced adenoma status selected from thelist consisting of CATD, CLUS, GDF15 and SAA1 from a biological samplecomprising circulating blood Computer-executable instructions forassessing an advanced adenoma risk associated with said measurement ofsaid panel of proteins An output unit for delivering a report assessingsaid advanced adenoma risk associated with said measurement of saidpanel of proteins. 147. The computer system of embodiment 146, whereinsaid panel comprises CATD, CLUS, GDF15 and SAA1. 148. The computersystem of any one of embodiments 146-147, wherein said panel comprisesno more than 12 proteins. 149. The computer system of any one ofembodiments 146-148, wherein the memory unit is configured for receivingdata comprising measurement of a second panel of proteins. 150. Thecomputer system of any one of embodiments 146-149, wherein said datacomprising measurement of a panel of proteins comprises ELISA data. 151.The computer system of any one of embodiments 146-150, wherein said datacomprising measurement of a panel of proteins comprises massspectrometry data. 152. The computer system of any one of embodiments146-151, wherein assessing an advanced adenoma risk comprises comparingsaid data to a reference panel associated with a known advanced adenomastatus. 153. The computer system of any one of embodiments 146-152,wherein said individual is assigned said known advanced adenoma statuswhen said data does not differ significantly from said reference panel.154. The computer system of any one of embodiments 146-152, wherein saidreference panel indicates presence of colorectal cancer. 155. Thecomputer system of any one of embodiments 146-152, wherein saidreference panel indicates absence of colorectal cancer. 156. Thecomputer system of any one of embodiments 146-155, wherein assessing anadvanced adenoma risk is performed on a computer configured to analyzereference panel information. 157. The method of any one of embodiments146-156, wherein said memory unit comprises at least one reference panelinformation set corresponding to a known advanced adenoma status. 158.The method of any one of embodiments 146-157, wherein the at least onereference panel information set comprises a machine learning model. 159.The method of any one of embodiments 146-158, wherein the machinelearning model is trained using at least 100 biomarker panelscorresponding to known colorectal health status. 160. The computersystem of any one of embodiments 146-159, wherein said report indicatesa sensitivity of at least 50% and a specificity of at least 80%. 161.The computer system of any one of embodiments 146-160, wherein saidreport indicates a sensitivity of at least 50%. 162. The computer systemof any one of embodiments 146-161, wherein said report indicates aspecificity of at least 80%. 163. The computer system of any one ofembodiments 146-162, wherein said report recommends that a colonoscopybe performed. 164. The computer system of any one of embodiments146-163, wherein said report recommends an independent surgicalintervention. 165. The computer system of any one of embodiments146-164, wherein said report recommends undergoing an independent cancerassay. 166. The computer system of any one of embodiments 146-165,wherein said report recommends undergoing a stool cancer assay. 167. Thecomputer system of any one of embodiments 146-166, wherein said reportrecommends administering an anticancer composition. 168. The computersystem of any one of embodiments 146-167, wherein said report recommendscontinued monitoring. 169. The computer system of any one of embodiments146-168, wherein at least one parameter of said individual's referencepanel information differs significantly from a corresponding value fromsaid reference panel information set, and wherein said individual'sreference panel information does not differ significantly from saidreference panel information set. 170. The computer system of any one ofembodiments 146-169, wherein no single protein of said panel indicatesthe individual's advanced adenoma status at a specificity of greaterthan 65% or a sensitivity of greater than 65%. 171. The computer systemof any one of embodiments 146-170, wherein the memory unit is configuredto receive age information from said individual. 172. The computersystem of any one of embodiments 146-171, wherein thecomputer-executable instructions factor in age of the individual whenassessing said advanced adenoma risk associated with said measurement ofsaid panel of proteins. 173. An ex vivo method of assessing a colorectalhealth risk status in a blood sample of an individual, comprising thesteps of obtaining a circulating blood sample from the individual;obtaining a biomarker panel level for a biomarker panel comprising alist of proteins in the sample comprising AACT, CO3, CO9, MIF, PSGL,SEPR, CEA, CATD, CLUS, GDF15 and SAA1, and obtaining an age for theindividual, wherein AACT, CO3, CO9, MIF, PSGL, SEPR, CEA, CATD, and agecomprise colorectal cancer panel information from said individual; andwherein CATD, CLUS, GDF15 and SAA1 comprise advanced adenoma panelinformation from said individual; comparing said colorectal cancer panelinformation from said individual to a reference colorectal cancer panelinformation set corresponding to a known colorectal cancer status;comparing said advanced adenoma panel information from said individualto a reference advanced adenoma panel information set corresponding to aknown advanced adenoma status; and categorizing said individual ashaving a colorectal health risk if either of said colorectal cancerpanel or said advanced adenoma panel does not differ significantly froma reference panel positive for a colorectal health risk. 174. The methodof any one of embodiments 173, wherein obtaining a circulating bloodsample comprises drawing blood from a vein or artery of the individual.175. The method of any one of embodiments 173-174, wherein the list ofproteins comprises no more than 20 proteins. 176. The method of any oneof embodiments 173-175, wherein the list of proteins comprises no morethan 11 proteins. 177. The method of any one of embodiments 173-176,wherein the categorizing has a sensitivity of at least 8% and aspecificity of at least 50%. 178. The method of any one of embodiments173-177, comprising transmitting a report of results of saidcategorizing to a health practitioner. 179. The method of any one ofembodiments 173-178, wherein the report recommends that a colonoscopy beperformed. 180. The method of any one of embodiments 173-179, whereinthe individual undergoes a colonoscopy. 181. The method of any one ofembodiments 173-178, wherein the report recommends an independentsurgical intervention. 182. The method of any one of embodiments173-181, wherein the individual undergoes an independent surgicalintervention. 183. The method of any one of embodiments 178-82, whereinthe report recommends undergoing an independent cancer assay. 184. Themethod of any one of embodiments 173-183, wherein the individualundergoes an independent cancer assay. 185. The method of any one ofembodiments 173-178, wherein the report recommends undergoing a stoolcancer assay. 186. The method of any one of embodiments 173-185, whereinthe individual undergoes a stool cancer assay. 187. The method of anyone of embodiments 173-178, wherein the report recommends administeringan anticancer composition. 188. The method of any one of embodiments173-187, wherein the individual is administered an anticancercomposition. 189. The method of any one of embodiments 173-178, whereinthe report recommends continued monitoring. 190. The method of any oneof embodiments 173-178, wherein at least one biomarker level of saidindividual's panel information differs significantly from acorresponding value from at least one of said reference panels, andwherein said individual's panel level as a whole does not differsignificantly from said reference panel level. 191. The method of anyone of embodiments 178-190, wherein no parameter of said individual'sreference panel information in isolation is indicative of saidcolorectal cancer status in said individual at a sensitivity of greaterthan 65% or a specificity of greater than 65%. 192. The method of anyone of embodiments 173-178, wherein the obtaining protein levelscomprises contacting a fraction of the circulating blood sample to a setof antibodies, wherein the set of antibodies comprises antibodiesspecific to AACT, CO3, CO9, MIF, PSGL, SEPR, CEA, CATD, CLUS, GDF15 andSAA1. 193. The method of any one of embodiments 173-178, wherein theobtaining protein levels comprises subjecting a fraction of thecirculating blood sample to a mass spectrometric analysis. 194. Themethod of any one of embodiments 173-178, wherein the obtaining proteinlevels comprises contacting the sample to protein binding DNA aptamers.195. The method of any one of embodiments 173-178, wherein the obtainingprotein levels comprises contacting the sample to an antibody array.196. The method of any one of embodiments 173-178, wherein the obtainingprotein levels comprises subjecting a fraction of the circulating bloodsample to a mass spectrometric analysis. 197. The method of any one ofembodiments 173-178, wherein at least one of said comparing and saidcategorizing is performed on a computer configured to analyze referencepanel information. 198. The method of any one of embodiments 173-178,wherein said reference panel information set corresponding to a knowncolorectal cancer status comprises a product of a machine learningmodel. 199. The method of any one of embodiments 173-198, wherein themachine learning model is trained using at least 100 biomarker panelscorresponding to known colorectal health status. 200. The embodiment ofany one of 1-199, wherein the panel comprises more biomarkers than thoselisted, but wherein a significant colorectal health assessment arisesfrom the listed biomarkers, alone or in combination with age. 201. Anembodiment of any one of 1-200, wherein the panel distinguishes CRCsamples from samples derived from a CRC-negative individual that ispositive for at least one non-CRC cancer.

Reference Art and Definitions

Throughout this application, various embodiments of this invention maybe presented in a range format. It should be understood that thedescription in range format is merely for convenience and brevity andshould not be construed as an inflexible limitation on the scope of theinvention. Accordingly, the description of a range should be consideredto have specifically disclosed all the possible subranges as well asindividual numerical values within that range. For example, descriptionof a range such as from 1 to 6 should be considered to have specificallydisclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numberswithin that range, for example, 1, 2, 3, 4, 5, and 6. This appliesregardless of the breadth of the range.

The practice of the present invention can employ, unless otherwiseindicated, conventional techniques of immunology, biochemistry,chemistry, molecular biology, microbiology, cell biology, genomics andrecombinant DNA, which are within the skill of the art. See, forexample, Sambrook, Fritsch and Maniatis, MOLECULAR CLONING: A LABORATORYMANUAL, 4th edition (2012); CURRENT PROTOCOLS IN MOLECULAR BIOLOGY (F.M. Ausubel, et al. eds., (1987)); the series METHODS IN ENZYMOLOGY(Academic Press, Inc.): PCR 2: A PRACTICAL APPROACH (M. J. MacPherson,B. D. Hames and G. R. Taylor eds. (1995)), CULTURE OF ANIMAL CELLS: AMANUAL OF BASIC TECHNIQUE AND SPECIALIZED APPLICATIONS, 6th Edition (R.I. Freshney, ed. (2010), and Lange, et. al., Molecular Systems BiologyVol. 4:Article 222 (2008), which are hereby incorporated by reference.

Definitions

As used in the specification and claims, the singular forms “a”, “an”and “the” include plural references unless the context clearly dictatesotherwise. For example, the term “a sample” includes a plurality ofsamples, including mixtures thereof.

The terms “determining”, “measuring”, “evaluating”, “assessing,”“assaying,” and “analyzing” are often used interchangeably herein torefer to forms of measurement, and include determining if an element ispresent or not (for example, detection). These terms can includequantitative, qualitative or quantitative and qualitativedeterminations. Assessing is alternatively relative or absolute.“Detecting the presence of” includes determining the amount of somethingpresent, as well as determining whether it is present or absent.

The terms “panel”, “biomarker panel”, “protein panel”, “classifiermodel”, and “model” are used interchangeably herein to refer to a set ofbiomarkers, wherein the set of biomarkers comprises at least twobiomarkers. Exemplary biomarkers are proteins or polypeptide fragmentsof proteins that are uniquely or confidently mapped to particularproteins. However, additional biomarkers are also contemplated, forexample age or gender of the individual providing a sample. Thebiomarker panel is often predictive and/or informative of a subject'shealth status, disease, or condition.

The “level” of a biomarker panel refers to the absolute and relativelevels of the panel's constituent markers and the relative pattern ofthe panel's constituent biomarkers.

The terms “colorectal cancer” and “CRC” are used interchangeably herein.The term “colorectal cancer status”, “CRC status” can refer to thestatus of the disease in subject. Examples of types of CRC statusesinclude, but are not limited to, the subject's risk of cancer, includingcolorectal carcinoma, the presence or absence of disease (for example,polyp or adenocarcinoma), the stage of disease in a patient (forexample, carcinoma), and the effectiveness of treatment of disease.

The term “mass spectrometer” can refer to a gas phase ion spectrometerthat measures a parameter that can be translated into mass-to-charge(m/z) ratios of gas phase ions. Mass spectrometers generally include anion source and a mass analyzer. Examples of mass spectrometers aretime-of-flight, magnetic sector, quadrupole filter, ion trap, ioncyclotron resonance, electrostatic sector analyzer and hybrids of these.“Mass spectrometry” can refer to the use of a mass spectrometer todetect gas phase ions.

The term “tandem mass spectrometer” can refer to any mass spectrometerthat is capable of performing two successive stages of m/z-baseddiscrimination or measurement of ions, including ions in an ion mixture.The phrase includes mass spectrometers having two mass analyzers thatare capable of performing two successive stages of m/z-baseddiscrimination or measurement of ions tandem-in-space. The phrasefurther includes mass spectrometers having a single mass analyzer thatcan be capable of performing two successive stages of m/z-baseddiscrimination or measurement of ions tandem-in-time. The phrase thusexplicitly includes Qq-TOF mass spectrometers, ion trap massspectrometers, ion trap-TOF mass spectrometers, TOF-TOF massspectrometers, Fourier transform ion cyclotron resonance massspectrometers, electrostatic sector-magnetic sector mass spectrometers,and combinations thereof.

The term “biochip” can refer to a solid substrate having a generallyplanar surface to which an adsorbent is attached. In some cases, asurface of the biochip comprises a plurality of addressable locations,each of which location may have the adsorbent bound there. Biochips canbe adapted to engage a probe interface, and therefore, function asprobes. Protein biochips are adapted for the capture of polypeptides andcan be comprise surfaces having chromatographic or biospecificadsorbents attached thereto at addressable locations. Microarray chipsare generally used for DNA and RNA gene expression detection.

The term “biomarker” and “marker” are used interchangeably herein, andcan refer to a polypeptide, gene, nucleic acid (for example, DNA and/orRNA) which is differentially present in a sample taken from a subjecthaving a disease for which a diagnosis is desired (for example, CRC) ascompared to a comparable sample taken from control subject that does nothave the disease (for example, a person with a negative diagnosis orundetectable CRC, normal or healthy subject, or, for example, from thesame individual at a different time point). Common biomarkers hereininclude proteins, or protein fragments that are uniquely or confidentlymapped to a particular protein, transition ion of an amino acidsequence, or one or more modifications of a protein such asphosphorylation, glycosylation or other post-translational orco-translational modification. In addition, a protein biomarker can be abinding partner of a protein, protein fragment, or transition ion of anamino acid sequence.

The terms “polypeptide,” “peptide” and “protein” are often usedinterchangeably herein in reference to a polymer of amino acid residues.A protein, generally, refers to a full-length polypeptide as translatedfrom a coding open reading frame, or as processed to its mature form,while a polypeptide or peptide informally refers to a degradationfragment or a processing fragment of a protein that nonetheless uniquelyor identifiably maps to a particular protein. A polypeptide can be asingle linear polymer chain of amino acids bonded together by peptidebonds between the carboxyl and amino groups of adjacent amino acidresidues. Polypeptides can be modified, for example, by the addition ofcarbohydrate, phosphorylation, etc. Proteins can comprise one or morepolypeptides.

An “immunoassay” is an assay that uses an antibody to specifically bindan antigen (for example, a marker). The immunoassay can be characterizedby the use of specific binding properties of a particular antibody toisolate, target, and/or quantify the antigen.

The term “antibody” can refer to a polypeptide ligand substantiallyencoded by an immunoglobulin gene or immunoglobulin genes, or fragmentsthereof, which specifically binds and recognizes an epitope. Antibodiesexist, for example, as intact immunoglobulins or as a number ofwell-characterized fragments produced by digestion with variouspeptidases. This includes, for example, Fab″ and F(ab)″2 fragments. Asused herein, the term “antibody” also includes antibody fragments eitherproduced by the modification of whole antibodies or those synthesized denovo using recombinant DNA methodologies. It also includes polyclonalantibodies, monoclonal antibodies, chimeric antibodies, humanizedantibodies, or single chain antibodies. “Fc” portion of an antibody canrefer to that portion of an immunoglobulin heavy chain that comprisesone or more heavy chain constant region domains, but does not includethe heavy chain variable region.

The term “tumor” can refer to a solid or fluid-filled lesion orstructure that may be formed by cancerous or non-cancerous cells, suchas cells exhibiting aberrant cell growth or division. The terms “mass”and “nodule” are often used synonymously with “tumor”. Tumors includemalignant tumors or benign tumors. An example of a malignant tumor canbe a carcinoma which is known to comprise transformed cells.

The term “binding partners” can refer to pairs of molecules, typicallypairs of biomolecules that exhibit specific binding. Protein—proteininteractions can occur between two or more proteins, when bound togetherthey often to carry out their biological function. Interactions betweenproteins are important for the majority of biological functions. Forexample, signals from the exterior of a cell are mediated via ligandreceptor proteins to the inside of that cell by protein—proteininteractions of the signaling molecules. For example, molecular bindingpartners include, without limitation, receptor and ligand, antibody andantigen, biotin and avidin, and others.

The term “control reference” can refer to a known or determined amountof a biomarker associated with a known condition that can be used tocompare to an amount of the biomarker associated with an unknowncondition. A control reference can also refer to a steady-state moleculewhich can be used to calibrate or normalize values of a non-steady statemolecule. A control reference value can be a calculated value from acombination of factors or a combination of a range of factors, such as acombination of biomarker concentrations or a combination of ranges ofconcentrations.

The terms “subject,” “individual,” or “patient” are often usedinterchangeably herein. A “subject” can be a biological entitycontaining expressed genetic materials. The biological entity can be aplant, animal, or microorganism, including, for example, bacteria,viruses, fungi, and protozoa. The subject can be tissues, cells andtheir progeny of a biological entity obtained in vivo or cultured invitro. The subject can be a mammal. The mammal can be a human. Thesubject may be diagnosed or suspected of being at high risk for adisease. The disease can be cancer. The cancer can be CRC (CRC). In somecases, the subject is not necessarily diagnosed or suspected of being athigh risk for the disease.

The term “in vivo” is used to describe an event that takes place in asubject's body.

The term “ex vivo” is used to describe an event that takes place outsideof a subject's body. An “ex vivo” assay is not performed on a subject.Rather, it is performed upon a sample separate from a subject. Anexample of an ‘ex vivo’ assay performed on a sample is an ‘in vitro’assay.

The term “in vitro” is used to describe an event that takes placescontained in a container for holding laboratory reagent such that it isseparated from the living biological source organism from which thematerial is obtained. In vitro assays can encompass cell-based assays inwhich cells alive or dead are employed. In vitro assays can alsoencompass a cell-free assay in which no intact cells are employed.

The term specificity, or true negative rate, can refer to a test'sability to exclude a condition correctly. For example, in a diagnostictest, the specificity of a test is the proportion of patients known notto have the disease, who will test negative for it. In some cases, thisis calculated by determining the proportion of true negatives (i.e.patients who test negative who do not have the disease) to the totalnumber of healthy individuals in the population (i.e., the sum ofpatients who test negative and do not have the disease and patients whotest positive and do not have the disease).

The term sensitivity, or true positive rate, can refer to a test'sability to identify a condition correctly. For example, in a diagnostictest, the sensitivity of a test is the proportion of patients known tohave the disease, who will test positive for it. In some cases, this iscalculated by determining the proportion of true positives (i.e.patients who test positive who have the disease) to the total number ofindividuals in the population with the condition (i.e., the sum ofpatients who test positive and have the condition and patients who testnegative and have the condition).

The quantitative relationship between sensitivity and specificity canchange as different diagnostic cut-offs are chosen. This variation canbe represented using ROC curves. The x-axis of a ROC curve shows thefalse-positive rate of an assay, which can be calculated as(1−specificity). The y-axis of a ROC curve reports the sensitivity foran assay. This allows one to easily determine a sensitivity of an assayfor a given specificity, and vice versa.

As used herein, the term ‘about’ a number refers to that number plus orminus 10% of that number. The term ‘about’ a range refers to that rangeminus 10% of its lowest value and plus 10% of its greatest value.

As used herein, the terms “treatment” or “treating” are used inreference to a pharmaceutical or other intervention regimen forobtaining beneficial or desired results in the recipient. Beneficial ordesired results include but are not limited to a therapeutic benefitand/or a prophylactic benefit. A therapeutic benefit may refer toeradication or amelioration of symptoms or of an underlying disorderbeing treated. Also, a therapeutic benefit can be achieved with theeradication or amelioration of one or more of the physiological symptomsassociated with the underlying disorder such that an improvement isobserved in the subject, notwithstanding that the subject may still beafflicted with the underlying disorder. A prophylactic effect includesdelaying, preventing, or eliminating the appearance of a disease orcondition, delaying or eliminating the onset of symptoms of a disease orcondition, slowing, halting, or reversing the progression of a diseaseor condition, or any combination thereof. For prophylactic benefit, asubject at risk of developing a particular disease, or to a subjectreporting one or more of the physiological symptoms of a disease mayundergo treatment, even though a diagnosis of this disease may not havebeen made.

EXAMPLES Example 1

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured for a panel comprising AACT, CATD, CEA,CO3, CO9, MIF, PSGL, and SEPR. The patient's age is also factored in tothe assessment, with age (in units of time) treated as a biomarker ofthe panel much like the other markers. The patient's panel results arecompared to panel results of known status, and the patient iscategorized with an 81% sensitivity, a 78% specificity, and a 31%positive predictive value as having colon cancer.

A colonoscopy is recommended and evidence of colorectal cancer isdetected in the individual.

Example 2

The patient of Example 1 is prescribed a treatment regimen comprising asurgical intervention. A blood sample is taken from the patient prior tosurgical intervention and protein accumulation levels are measured for apanel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. Thepatient's age is also factored in to the assessment, with age treated asan ‘accumulation level’ of time rather than protein. The patient's panelresults are compared to panel results of known status, and the patientis categorized with an 81% sensitivity, a 78% specificity, and a 31%positive predictive value as having colon cancer.

A blood sample is taken from the patient subsequent to surgicalintervention and protein accumulation levels are measured for a panelcomprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. The patient'sage is also factored in to the assessment, with age treated as an‘accumulation level’ of time rather than protein. The patient's panelresults are compared to panel results of known status, and the patientis categorized with an 81% sensitivity, a 78% specificity, and a 31%positive predictive value as no longer having colon cancer.

Example 3

The patient of Example 1 is prescribed a treatment regimen comprising achemotherapeutic intervention comprising 5-FU administration. A bloodsample is taken from the patient prior to chemotherapeutic interventionand protein accumulation levels are measured for a panel comprisingAACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. The patient's age isalso factored in to the assessment, with age treated as an ‘accumulationlevel’ of time rather than protein. The patient's panel results arecompared to panel results of known status, and the patient iscategorized an 81% sensitivity, a 78% specificity, and a 31% positivepredictive value as having colon cancer.

A blood sample is taken from the patient at weekly intervals duringchemotherapy treatment and protein accumulation levels are measured fora panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. Thepatient's age is also factored in to the assessment, with age treated asan ‘accumulation level’ of time rather than protein. The patient's panelresults are compared to panel results of known status. The patient'spanel results over time indicate that the cancer has responded to thechemotherapy treatment and that the colorectal cancer is no longerdetectable by completion of the treatment regimen.

Example 4

The patient of Example 1 is prescribed a treatment regimen comprising achemotherapeutic intervention comprising oral capecitabineadministration. A blood sample is taken from the patient prior tochemotherapeutic intervention and protein accumulation levels aremeasured for a panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL,and SEPR. The patient's age is also factored in to the assessment, withage treated as an ‘accumulation level’ of time rather than protein. Thepatient's panel results are compared to panel results of known status,and the patient is categorized with an 81% sensitivity, a 78%specificity, and a 31% positive predictive value as having colon cancer.

A blood sample is taken from the patient at weekly intervals duringchemotherapy treatment and protein accumulation levels are measured fora panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. Thepatient's panel results are compared to panel results of known status.The patient's panel results over time indicate that the cancer hasresponded to the chemotherapy treatment and that the colorectal canceris no longer detectable by completion of the treatment regimen.

Example 5

The patient of Example 1 is prescribed a treatment regimen comprising achemotherapeutic intervention comprising oral oxaliplatinadministration. A blood sample is taken from the patient prior tochemotherapeutic intervention and protein accumulation levels aremeasured for a panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL,and SEPR. The patient's age is also factored in to the assessment, withage treated as an ‘accumulation level’ of time rather than protein. Thepatient's panel results are compared to panel results of known status,and the patient is categorized with an 81% sensitivity, a 78%specificity, and a 31% positive predictive value as having colon cancer.

A blood sample is taken from the patient at weekly intervals duringchemotherapy treatment and protein accumulation levels are measured fora panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. Thepatient's age is also factored in to the assessment, with age treated asan ‘accumulation level’ of time rather than protein. The patient's panelresults are compared to panel results of known status. The patient'spanel results over time indicate that the cancer has responded to thechemotherapy treatment and that the colorectal cancer is no longerdetectable by completion of the treatment regimen.

Example 6

The patient of Example 1 is prescribed a treatment regimen comprising achemotherapeutic intervention comprising oral oxaliplatin administrationin combination with bevacizumab. A blood sample is taken from thepatient prior to chemotherapeutic intervention and protein accumulationlevels are measured for a panel comprising AACT, CATD, CEA, CO3, CO9,MIF, PSGL, and SEPR. The patient's age is also factored in to theassessment, with age treated as an ‘accumulation level’ of time ratherthan protein. The patient's panel results are compared to panel resultsof known status, and the patient is categorized with an 81% sensitivity,a 78% specificity, and a 31% positive predictive value as having coloncancer.

A blood sample is taken from the patient at weekly intervals duringchemotherapy treatment and protein accumulation levels are measured fora panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. Thepatient's age is also factored in to the assessment, with age treated asan ‘accumulation level’ of time rather than protein. The patient's panelresults are compared to panel results of known status. The patient'spanel results over time indicate that the cancer has responded to thechemotherapy treatment and that the colorectal cancer is no longerdetectable by completion of the treatment regimen.

Example 7

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured using reagents in an ELISA kit todetect members of a panel comprising AACT, CATD, CEA, CO3, CO9, MIF,PSGL, and SEPR. The patient's age is also factored in to the assessment,with age treated as an ‘accumulation level’ of time rather than protein.The patient's panel results are compared to panel results of knownstatus, and the patient is categorized with an 81% sensitivity, a 78%specificity, and a 31% positive predictive value as having colon cancer.A colonoscopy is recommended and evidence of colorectal cancer isdetected in the individual.

Example 8

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured using mass spectrometry to detectmembers of a panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, andSEPR. The patient's age is also factored in to the assessment, with agetreated as an ‘accumulation level’ of time rather than protein. Thepatient's panel results are compared to panel results of known status,and the patient is categorized with an 81% sensitivity, a 78%specificity, and a 31% positive predictive value as having colon cancer.A colonoscopy is recommended and evidence of colorectal cancer isdetected in the individual.

Example 9

1000 patients at risk of colorectal cancer are tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured to detect members of a panel comprisingAACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. The patient's age isalso factored in to the assessment, with age treated as an ‘accumulationlevel’ of time rather than protein. The patients' panel results arecompared to panel results of known status, and the patients arecategorized with an 81% sensitivity, a 78% specificity, and a 31%positive predictive value into a colon cancer category. A colonoscopy isrecommended for patients categorized as positive. Of the patientscategorized as having colon cancer, 80% are independently confirmed tohave colon cancer. Of the patients categorized as not having coloncancer, 20% are later found to have colon cancer through an independentfollow up test, confirmed via a colonoscopy.

Example 10

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured for a panel comprising CATD, CLUS,GDF15, and SAA1. The patient's panel results are compared to panelresults of known status, and the patient is categorized with a 50%sensitivity and an 80% specificity as having advanced colorectaladenoma. A colonoscopy is recommended and evidence of advancedcolorectal adenoma is detected in the individual.

Example 11

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured for a panel comprising CATD, CLUS,GDF15, and SAA1. The patient's panel results are compared to panelresults of known status, and the patient is categorized with a 45%sensitivity and an 80% specificity as having advanced colorectaladenoma. Further monitoring is recommended and the health professionalobtains subsequent blood or stool tests for colorectal cancer and/oradvanced adenoma.

Example 12

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured using reagents in an ELISA kit todetect members of a panel comprising CATD, CLUS, GDF15, and SAA1. Thepatient's panel results are compared to panel results of known status,and the patient is categorized with a 45% sensitivity and an 80%specificity as having advanced colorectal adenoma. A colonoscopy isrecommended and evidence of advanced colorectal adenoma is detected inthe individual.

Example 13

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient and proteinaccumulation levels are measured using mass spectrometry to detectmembers of a panel comprising CATD, CLUS, GDF15, and SAA1. The patient'spanel results are compared to panel results of known status, and thepatient is categorized with a 45% sensitivity and an 80% specificity ashaving advanced colorectal adenoma. A colonoscopy is recommended andevidence of colorectal cancer is detected in the individual.

Example 14

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient. The bloodsample is mailed to a facility, where protein accumulation levels aremeasured using mass spectrometry to detect members of a panel comprisingAACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. The patient's age isalso factored in to the assessment, with age treated as an ‘accumulationlevel’ of time rather than protein. The patient's panel results arecompared to panel results of known status, and the patient iscategorized with an 81% sensitivity, a 78% specificity, and a 31%positive predictive value as having colon cancer. A colonoscopy isrecommended and evidence of colorectal cancer is detected in theindividual.

Example 15

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient. The bloodsample is mailed to a facility, where protein accumulation levels aremeasured using ELISA to detect members of a panel comprising AACT, CATD,CEA, CO3, CO9, MIF, PSGL, and SEPR. The patient's age is also factoredin to the assessment, with age treated as an ‘accumulation level’ oftime rather than protein. The patient's panel results are compared topanel results of known status, and the patient is categorized with an81% sensitivity, a 78% specificity, and a 31% positive predictive valueas having colon cancer. A colonoscopy is recommended and evidence ofcolorectal cancer is detected in the individual.

Example 16

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient. The bloodsample is mailed to a facility, where plasma is prepared and proteinaccumulation levels are measured using ELISA to detect members of apanel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. Thepatient's age is also factored in to the assessment, with age treated asan ‘accumulation level’ of time rather than protein. The patient's panelresults are compared to panel results of known status, and the patientis categorized with an 81% sensitivity, a 78% specificity, and a 31%positive predictive value as having colon cancer. A colonoscopy isrecommended and evidence of colorectal cancer is detected in theindividual.

Example 17

A patient at risk of colorectal cancer is tested using a panel asdisclosed herein. A blood sample is taken from the patient. The bloodsample is mailed to a facility, where plasma is prepared and proteinaccumulation levels are measured using mass spectrometry to detectmembers of a panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, andSEPR. The patient's age is also factored in to the assessment, with agetreated as an ‘accumulation level’ of time rather than protein. Thepatient's panel results are compared to panel results of known status,and the patient is categorized with an 81% sensitivity, a 78%specificity, and a 31% positive predictive value as having colon cancer.A colonoscopy is recommended and evidence of colorectal cancer isdetected in the individual.

Example 18

Potential protein biomarkers were tested in an intent-to-test studydesign that included factors that would be present in anabove-average-risk population (e.g., co-morbidities, other GIpathologies, age). 1,045 samples were evaluated by ELISA. Age was addedas a model parameter in a case-control discovery partition of 309patients (see FIG. 1). Indeterminate call boundaries were added in anintent-to-test discovery partition of 373 patients. The final proteinbiomarker panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL, andSEPR, and the age of the subject, was validated in 373 patients to havean 81% sensitivity and a 78% specificity with a 15% indeterminate callrate. No statistical difference was detected between early and late CRCperformance.

Example 19

For a CRC protein marker panel discovery and validation study, 137 CRCpatient plasma samples and 137 age- and gender-matched controls fromthree different commercial sample biobanks were acquired to conduct astudy with case-control design. Samples were selected across therelevant age range for CRC screening guidelines, 50-75, across thestages of CRC, I-IV, and across the site of cancer, colon versus rectum.The patients were divided into a discovery partition of 138 pairedsamples and a validation partition of 136 paired samples. A 187 proteintargeted MS assay was used to collect data from all 274 patientsselected for this validation study, and the 138 paired patient samplesin the discovery partition were used, to determine the abundance levelsfor the proteins to be evaluated in a variety of feature selection andclassifier assembly workflows.

Based on the analysis, 12 models were built and selected for validation30 of the original 187 proteins. These 12 models had AUCs that rangedfrom about 0.77 to 0.83. Classifier models were then selected and theirprotein components and algorithms locked to evaluate them using the datacollected from the held-out validation partition. The samples wereblinded to the laboratory and analysis staff. All 12 models validatedsuccessfully and their AUC's were not significantly different thanpredicted from the discovery partition. One classifier with 13 componentproteins had a validation AUC of 0.91 and a test performance of 87%sensitivity and 81% specificity at the point of maximum accuracy. Thisclassifier's performance on early CRC was 90% sensitivity (46 out of 51stage I/II cancers correctly classified).

To confirm clinical validity, selected proteins were evaluated in a newcohort of samples and with another detection technology, ELISA. Thisapproach helps ensure the results achieved in the first study were notthe result of technological or study design bias. For a secondvalidation sample set, patient plasma samples were obtained from aDanish study, Endoscopy II, performed by Dr. Hans Nielsen of HvidovreHospital/University of Copenhagen. This study collected samples from 4,698 patients who were referred for diagnostic colonoscopy based on atleast one symptom of bowel neoplasia. Plasma was collected prior tocolonoscopy and processed to plasma and stored using validated standardoperating procedures. Using this cohort of patient samples, 150 CRCplasma samples and 150 age- and gender-matched controls were selectedfor a second discovery and validation study. The samples collectedranged from patient ages 50 to 75, across all four CRC stages, andacross the colon and rectum. The controls were designed from the subsetof patients who had no comorbidities and no findings on colonoscopy inorder to most closely mimic anticipated intent-to-test population:patients with above-average risk but no prior clear indications forcolonoscopy. Commercially available ELISA reagents were used for 28 ofthe 30 proteins that comprised the 12 classifiers from the first study.

Using the 300-patient plasma samples selected from the Endoscopy IIstudy and the 28 ELISAs for proteins previously validated, proteinabundance data was collected target. Based on new ELISA data for the 28proteins in the 150 sample discovery partition, a machine learningapproach was used in ten rounds of 10-fold cross validation to build 5models for evaluation. The models ranged in size from 7 to 18 proteinsand produced a range of discovery performance from 0.83 to 0.86, basedon Receiver Operating Characteristic, or ROC, area under the curve, orAUC. An ideal test, with 100% sensitivity and 100% specificity wouldbegin in the lower left corner, go straight to the upper left corner,then to the upper right corner, and the AUC would be 1.00. On the otherhand, a test without predictive value would be a straight diagonal linefrom the lower left corner to the upper right corner, with an AUC of0.50. Once models were selected and their components and algorithm werelocked, the data from the validation partition were used to evaluate themodels.

CRC marker proteins were further validated for their ability to comprisepanels that have significant detection performance for advanced adenoma,the precursor lesion to CRC. In the natural history of CRC developmentit is generally accepted that all CRC's come from advanced adenomas butnot all advanced adenomas become CRCs. Nevertheless, several studieshave demonstrated that the removal of advanced adenomas during screeningcolonoscopy significantly reduces the incidence of subsequent colorectalcancer.

Using the Danish Endoscopy II study, a new 302 patient, age- andgender-matched, site-stratified, subset of samples was selected usingthe definition for advanced adenoma commonly used in other recent,external studies. Using the same ELISAs for the 28 proteins as in theprior CRC validation study, data were collected from each of the 302samples, divided into a 150-sample discovery partition and a 152-samplevalidation partition. Using the same methods for classifier assembly incross-validation and final validation as described above, an advancedadenoma classifier was identified that comprises 4 of the 28 proteinsand has 45% sensitivity and 80% specificity (ROC AUC 0.65) Example 20

A total of 6 biomarkers were selected at random from a panel comprising:AACT, CATD, CEA, CO3, CO9, MIF, PSGL, and SEPR. A total of 6 biomarkerswere also selected at random from the mass spec analyzed validationstudy comprising 187 proteins. The panel comprising 6 proteins selectedfrom a biomarker panel comprising AACT, CATD, CEA, CO3, CO9, MIF, PSGL,and SEPR was validated in 373 patients and performed 95% better than the6 biomarkers selected at random from the mass spec analyzed validationstudy comprising 187 proteins.

Example 21—Panel Comparison

Panels disclosed herein were compared to randomly determined panelsderived from enriched biomarker lists to assess their performancerelative to background chance.

As discussed above and as demonstrated in FIG. 1, panels disclosedherein were derived by generating a 187 member list of markersidentified in the literature as being of potential relevance to cancerdetection. Biomarkers in this list were then assayed in a sample setderived from individuals of known colorectal health status, and 28biomarkers that correlated strongly with sample colorectal health statuswere identified. These 28 markers were assayed through an ELISA basedapproach on a second set of samples derived from a second set ofindividuals of known colorectal health status, and the panels disclosedherein were produced.

Thus, the MS-identified 28 marker set was already substantially enrichedover the initial 187 member set identifiable to one of skill in the art.Nonetheless, an investigation was made into the performance of thepanels disclosed herein relative to the MS-enriched biomarker dataset.

Panels of various sizes were generated from the 28-member MS enrichedset, and these panels were assessed as to their predictive value on amarker-quantified sample set derived from individuals of knowncolorectal health status. Random panels were generated using RandomForest models and using SVM models. AUC values were determined for eachrandom panel. AUC distribution curves for panels of a given size weregenerated.

The AUC distribution curves are presented in FIG. 20. The three topgraphs represent panels generated through SVM, while the three bottomgraphs depict panels generated through Random Forest modeling. For eachplot, the panel size is listed at top with grey back shading. The Y axisindicates number of panels, while the X axis indicates AUC value for thepanel columns indicated. The dashed line indicates the AUC value which95% of the randomly generated panels from the MS-enriched dataset fallbelow.

The results are summarized in Tables 13 and 14.

TABLE 13 SVM MS-Enriched Panel Characteristics Panel Number Min Max MeanStdev Median 95% Size of Combos AUC AUC AUC AUC AUC AUC 2 378 0.3800.741 0.544 0.074 0.545 0.667 3 3276 0.389 0.806 0.594 0.079 0.600 0.7144 10000 0.387 0.822 0.637 0.076 0.644 0.748 5 10000 0.401 0.834 0.6690.068 0.676 0.769 6 10000 0.406 0.837 0.694 0.061 0.701 0.782 7 100000.416 0.843 0.711 0.055 0.716 0.792 8 10000 0.416 0.851 0.725 0.0490.730 0.797 9 10000 0.409 0.848 0.734 0.045 0.737 0.800 10 10000 0.4270.848 0.743 0.041 0.746 0.803

TABLE 14 Random forest-Enriched Panel Characteristics Panel Number MinMax Mean Stdev Median 95% Size of Combos AUC AUC AUC AUC AUC AUC 2 3780.410 0.799 0.610 0.083 0.616 0.746 3 3276 0.391 0.832 0.644 0.077 0.6540.755 4 10000 0.388 0.840 0.668 0.070 0.678 0.765 5 10000 0.395 0.8350.685 0.062 0.693 0.774 6 10000 0.397 0.834 0.697 0.059 0.703 0.781 710000 0.439 0.836 0.708 0.054 0.713 0.789 8 10000 0.443 0,.839 0.7180.049 0.722 0.791 9 10000 0.448 0.835 0.723 0.046 0.725 0.794 10 100000.483 0.833 0.730 0.043 0.732 0.796

As indicated in the graphs and models, panels of 8-10 membersdemonstrate mean and median AUC values of about 0.71-0.73. 95% of thecurves display an AUC of 0.80 or less.

Referring to FIG. 2, one sees that a lead 9 member panel disclosedherein for the assessment of colorectal health has a validated AUC valueof 0.83. This value is greater than the 95% threshold AUC of comparable9 and even 10 member panels, and is comparable to the maximum AUC valuesobserved for the entire datasets.

Referring also to Table 8, one sees that comparable AUC values, farsuperior to those of the randomly generated panels, are obtained forModels 1-13. Model 12, it is observed, differs from the panel of FIG. 2in that age is excluded as a biomarker.

This analysis makes clear that panels herein outperform randomlygenerated panels, even randomly derived panels selected from biomarkersthat are already experimentally enriched to the 28 best targeted-MSidentified markers from a 187 member set identified in the art. That is,even upon 6× enrichment of markers above a set taught in the art, panelsherein outperform essentially 100% of the randomly generated panelsderived therefrom.

Example 22—CRC and AA Test Implementation

Throughout this example, patients 1, 2, and 3 are representative ofpatient data generated through the methods, kits, systems andcompositions herein but in the interest of patient confidentiality, noneof patients 1, 2 and 3 represent any patient's actual data.

An exemplary first patient, second patient and third patient eachprovide a blood sample for analysis. The samples are shipped to aprocessing center and ELISA reagents are used to determine CRC and AApanel levels using reagents to determine levels of AACT, CEA, CO3, CO9,MIF, PSGL, SEPR, CATD, CLUS, GDF15, SAA1. Patient age is also provided.

Biomarkers are measured and the results presented in Table 15.

TABLE 15 CRC/AA Test Input Measurements Panel CRC CRC/AA AA Patient AACTCEA CO9 SEPR CO3 MIF PSGL Age CATD CLUS GDF15 SAA1 1 246600 19 161800105500 820 8 500 46 37100 5440 119 4537 2 171300 7 20800 108100 270 90290 68 6190 8450 179 2290 3 215000 7 54100 16600 490 85 500 79 451005310 24 4178

The biomarker panel levels for each of the three patients are assignedModel Scores according to a Machine Learning Model assembled from panellevels of samples from reference individuals of known colorectal healthstatus as depicted in FIG. 1. From the Machine Learning Model, a cutoffscore of 2.9 is calculated as the lower limit for a positive CRC score.Scores below this cutoff are called negative for colorectal cancer. An‘indeterminate range’ is identified among the negative scores, such thatpatient scores falling within the intermediate range are marked forfurther analysis. The indeterminate range spans scores of 1.24-2.46.Scores above the intermediate range but below the positive cutoff are insome cases additionally scrutinized. Through a similar approach, acutoff score of 0.25 is calculated as the lower limit for a positive AAscore.

Patient panel levels are assessed and a score assigned to each panel forCRC and AA. Depending on the score, a follow up assay is recommended anda diagnosis is generated according to this follow-up assay. The resultsare presented in Table 16.

TABLE 16 CRC/AA Test Output Scores and Measurements CRC CRC CRC AA AAPatient Score Call Diagnosis Score AA Call Diagnosis 1 1.7 IndeterminateAdenoma 1.0 Positive Adv. Adenoma 2 0.7 Negative No 0.3 Negative Nofindings findings 3 5.9 Positive Colon 0.9 Positive No Cancer findings

Patient 1 is assigned a CRC model score of 1.7. The score is below the2.9 cutoff score for a positive call, but is scored as indeterminate.Patient 1 is assigned an AA score of 1.0, and is called positive foradvanced adenoma. A report is generated and provided to the patient.

The patient undergoes a colonoscopy. No colorectal cancer is detected,but a noncancerous adenoma is detected. The adenoma is removed and thepatient is later confirmed to be colon cancer and adenoma free by afollow-up test. The patient is observed for 5 years and no symptoms orchange in colorectal cancer status is observed, indicating that the testcorrectly identified the patient's status as negative for colon cancer.

Patient 2 is assigned a CRC model score of 0.72 and is called negativefor colorectal cancer. Patient 2 is assigned an AA model score of 0.29and is called negative for advanced adenoma. A report is generated andprovided to the patient.

The patient follows up with a stool sample test and the results aresimilarly negative. The patient is observed for 5 years and no symptomsor change in colorectal health status is observed, indicating that thetest correctly predicted no colorectal cancer and no advanced adenoma inthe individual.

Patient 3 is assigned a CRC model score of 5.9 and is called positivefor colorectal cancer. Patient 3 is assigned an AA score of 0.9 and iscalled positive for AA. A report is generated and provided to thepatient.

The patient undergoes a colonoscopy. Early stage colorectal cancer isdetected, but no adenoma is detected.

The patient undergoes colon cancer treatment and symptoms arealleviated. A second blood sample is taken from the patient followingtreatment and a CRC score below 2.9 is assigned. A colonoscopy confirmsthat the colorectal cancer is no longer present in the individual.

This example demonstrates various features of the panels herein. The CRCand AA panels are used in combination and share common markers. Thepanels are derived from blood and are shipped to be tested elsewhere. Areport is generated and provided to the patient. The results areindependently corroborated using an invasive approach such as acolonoscopy or noninvasive approach such as a stool test. The testresults are largely corroborated by independent assays. Example 23—CRCand AA score analysis

The data in Table 15 allows further analysis of the CRC and AA panelperformances.

For instance, an examination of Table 15 is illustrative of relevantaspects of panel performance relative to the predictive value of itsindividual markers.

One sees that for some markers, the individual marker level correspondswith the overall panel result. For example, SEPR levels for patient 1and patient 2 are similar at about 10,000, while patient 3 scoressubstantially lower at 1,600. This grouping is consistent with theoverall scoring of patient 1 and patient 2 as negative or indeterminatefor CRC, while patient 3 scored positive.

However, in the majority of the cases, individual marker levels do notpredict the outcome that one finds upon analyzing the panel level as awhole. For biomarkers AACT, CO9, and CO3, patient 3 levels areintermediate between those of patient 1 and patient 2. For biomarkersCEAMIF and PSGL, patient 3 levels roughly match those of either patient1 or patient 2.

Thus, looking at these biomarkers individually, one does not find anindication that patient 3 rather than patient 1 or patient 2 is likelypositive for CRC.

These measurements indicate that the CRC panel as a whole possesses apredictive value that surpasses that of its constituent biomarkermembers. Furthermore, the CRC biomarker panel as a whole provides apredictive value that in some cases, contradicts the prediction of itsindividual members. Accordingly, the CRC biomarker panel as a wholeprovides a predictive value that is better than its components and thatis more than a simple collection of its individual marker results.Example 24—Clinical Utility of Noninvasive, Accurate Colorectal HealthAssay

A recalcitrant patient demonstrated symptoms of CRC but refused acolonoscopy. The patient's primary care physician ordered a SimpliProcolorectal health assessment test. The results indicated that thepatient was at a high risk for CRC and for AA. The patient consultedwith family and was convinced to schedule a colonoscopy. The colonoscopyrevealed polyps and an early stage cancerous mass, all of which wereremoved during the procedure. A follow-up colorectal health assessmentindicated that the patient is cancer free. The patient's early stagecancerous mass would likely have progressed to advanced disease with ahigh probability of death without the colonoscopy and concurrentpolypectomy.

This Example demonstrates the benefit to the public of offering anoninvasive colorectal health assay that is both sensitive and specific,and is easily complied with. In combination with Example 25, below, thisexample demonstrates that the reluctance to undergo a colonoscopy iscommon, and that it can have severe health consequences if it results inan early stage cancer not being detected when it is relatively easilytreated.

Example 25—Clinical Utility of Noninvasive, Accurate Colorectal HealthAssay

A recalcitrant patient demonstrated symptoms of CRC but delayed acolonoscopy for over 6 months. The patient's primary care physicianordered a SimpliPro colorectal health assessment test. The resultsindicated that the patient was at a high risk for CRC and for AA. Thepatient scheduled a colonoscopy. During the procedure, a 6 cm malignantmass was identified and removed. A follow-up colorectal healthassessment indicated that the patient is cancer free. The patient'searly stage cancerous mass would likely have progressed to advanceddisease with a high probability of death without the colonoscopy andconcurrent polypectomy.

This Example demonstrates the benefit to the public of offering anoninvasive colorectal health assay that is both sensitive and specific,and is easily complied with. In combination with Example 24, above, thisexample demonstrates that the reluctance to undergo a colonoscopy iscommon, and that it can have severe health consequences if it results inan early stage cancer not being detected when it is relatively easilytreated.

While preferred embodiments of the disclosure have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the disclosure. It should beunderstood that various alternatives to the embodiments of thedisclosure described herein may be employed in practicing thedisclosure. It is intended that the following claims define the scope ofthe disclosure and that methods and structures within the scope of theseclaims and their equivalents be covered thereby.

What is claimed is:
 1. A method of assessing a colorectal cancer riskstatus in an individual, comprising the steps of obtaining a circulatingblood sample from the individual; obtaining a biomarker panel level fora biomarker panel comprising a list of proteins in the sample comprisingAACT, CO3, CO9, MIF, and PSGL to comprise panel information from saidindividual; comparing said panel information from said individual to areference panel information set corresponding to a known colorectalcancer status; and categorizing said individual as having saidcolorectal cancer risk status if said individual's reference panelinformation does not differ significantly from said reference panelinformation set.
 2. The method of claim 1, wherein obtaining acirculating blood sample comprises drawing blood from a vein or arteryof the individual
 3. The method of claim 1, wherein the panelinformation comprises age information for the individual.
 4. The methodof claim 1, wherein the list of proteins comprises AACT, CO3, CO9, MIF,PSGL, CATD, CEA and SEPR.
 5. The method of claim 1, wherein thecategorizing has a sensitivity of at least 81% and a specificity of atleast 78%.
 6. The method of claim 1, comprising transmitting a report ofresults of said categorizing to a health practitioner.
 7. The method ofclaim 6, wherein the report recommends that a colonoscopy be performed.8. The method of claim 1, wherein the individual undergoes acolonoscopy.
 9. The method of claim 1, wherein no parameter of saidindividual's reference panel information in isolation is indicative ofsaid colorectal cancer status in said individual at a sensitivity ofgreater than 65% or a specificity of greater than 65%.
 10. The method ofclaim 1, wherein the obtaining protein levels comprises contacting afraction of the circulating blood sample to a set of antibodies, whereinthe set of antibodies comprises antibodies specific to AACT, CO3, CO9,MIF, and PSGL.
 11. A method of monitoring efficacy of a colorectalcancer treatment regimen in an individual, comprising the steps ofobtaining a first sample comprising circulating blood from theindividual at a first time point; administering the treatment regimen tothe individual; obtaining a second sample comprising circulating bloodfrom the individual at a second time point; obtaining a first panellevel comprising protein levels for a list of proteins in the firstsample and a second panel level comprising protein levels for a list ofproteins in the second sample, said list comprising AACT, CO3, CO9, MIF,and PSGL to comprise panel information for said first sample and saidsecond sample; wherein a change in protein levels indicates efficacy ofthe colorectal cancer treatment.
 12. The method of claim 11, whereinobtaining the first sample comprises drawing blood from a vein or arteryof the individual.
 13. The method of claim 11, wherein the treatmentregimen comprises a colonoscopy.
 14. The method of claim 11, wherein thelist of proteins comprises AACT, CO3, CO9, MIF, PSGL, CATD, CEA andSEPR.
 15. The method of claim 11, comprising changing the treatmentregimen if no efficacy is indicated.
 16. The method of claim 11,comprising repeating the treatment regimen if no efficacy is indicated.17. The method of claim 11, comprising discontinuing the treatmentregimen if efficacy is indicated.
 18. A method of assessing an advancedadenoma risk status in an individual, comprising the steps of obtaininga circulating blood sample from the individual; obtaining protein levelsfor a list of proteins relevant to advanced adenoma in the samplecomprising at least three of CATD, CLUS, GDF15 and SAA1 to comprisebiomarker panel information from said individual; comparing said panelinformation from said individual to a reference panel information setcorresponding to a known advanced adenoma status; and categorizing saidindividual as having said advanced adenoma risk status if saidindividual's reference panel information does not differ significantlyfrom said reference panel information set.
 19. The method of claim 18,wherein obtaining a circulating blood sample comprises drawing bloodfrom a vein or artery of the individual
 20. The method of claim 18,wherein the panel information comprises age information for theindividual.
 21. The method of claim 18, comprising transmitting a reportof results of said categorizing to a healthcare professional.
 22. Themethod of claim 18, wherein the individual undergoes a colonoscopy. 23.A method of assessing a colorectal health risk status in an individual,comprising the steps of obtaining a circulating blood sample from theindividual; obtaining a biomarker panel level for a biomarker panelcomprising a list of proteins in the sample comprising AACT, CO3, CO9,MIF, PSGL, SEPR, CEA, CATD, CLUS, GDF15 and SAA1, and obtaining an agefor the individual, wherein AACT, CO3, CO9, MIF, PSGL, SEPR, CEA, CATD,and age comprise colorectal cancer panel information from saidindividual; and wherein CATD, CLUS, GDF15 and SAA1 comprise advancedadenoma panel information from said individual; comparing saidcolorectal cancer panel information from said individual to a referencecolorectal cancer panel information set corresponding to a knowncolorectal cancer status; comparing said advanced adenoma panelinformation from said individual to a reference advanced adenoma panelinformation set corresponding to a known advanced adenoma status; andcategorizing said individual as having a colorectal health risk ifeither of said colorectal cancer panel or said advanced adenoma paneldoes not differ significantly from a reference panel positive for acolorectal health risk.
 24. The method of claim 23, wherein obtaining acirculating blood sample comprises drawing blood from a vein or arteryof the individual.
 25. The method of claim 23, wherein the list ofproteins comprises no more than 20 proteins.
 26. The method of claim 23,comprising transmitting a report of results of said categorizing to ahealth practitioner.
 27. The method of claim 26, wherein the reportrecommends that a colonoscopy be performed.
 28. The method of claim 23,wherein the individual undergoes a colonoscopy.
 29. The method of claim26, wherein the individual undergoes a stool cancer assay.
 30. Themethod of claim 26, wherein the obtaining protein levels comprisescontacting a fraction of the circulating blood sample to a set ofantibodies, wherein the set of antibodies comprises antibodies specificto AACT, CO3, CO9, MIF, PSGL, SEPR, CEA, CATD, CLUS, GDF15 and SAA1.